{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5b2b6c31-2e20-4895-96ea-304883709351",
   "metadata": {},
   "source": [
    "# Cyber Threat Hunting - Chapter 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scenario"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.2 Jupyter notebook code — Load the data from the original training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3c29c89f-17bb-4442-b639-26c140f9990c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "path_train = r\"dtqbc-m-train.csv\"\n",
    "df_orig = pd.read_csv(path_train, usecols = \\\n",
    "    ['label','qname', 'qd_qtype'])\n",
    "df_orig.rename(columns={'qname': 'fqdn',\\\n",
    "    'qd_qtype':'query_type', 'label':'class'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f285f8a-d801-4598-82d6-bba4b49140ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>fqdn</th>\n",
       "      <th>query_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>tv66ShUMWEDcg64hME6+9lvc4sPOHS/9HXrtTHmIzMEKPa...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>q+Z9iX2hCN0JA2hJIuIp9Bb/2wLoDfrfEv8LR5rSJjrPE0...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>q+aDOYNRCB52vD7EdADs5iUQKCj0LwoTa5uezQMcdKxRDJ...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0yta0P��XAG2B2�n��zz4�7�Z�����o�J��8e�fu�7�o�g...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>kzNHyLC/RydeypLK50E/36+P49TxXHE90KWkM+mmE7cDib...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>2</td>\n",
       "      <td>45Ar/qLwLA64Iu9togr3V0LDqeeSBUdLVdAPWqq7xhiATw...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>2</td>\n",
       "      <td>PsfIoHptQh0HJbI7PSStBnxyIoUx5xrdtgxZuOxd14cwH5...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>4</td>\n",
       "      <td>r43763.tunnel.tuns.org.</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>1</td>\n",
       "      <td>q+aNi42jCMDVPbSPQtej99RiHkOiezYpVcsfplrVguvVjo...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000</th>\n",
       "      <td>2</td>\n",
       "      <td>XFDrQQGp/TPekj9Vol6lzOtmzsv1g6ndrO16TjCc905H9I...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20001 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class                                               fqdn  query_type\n",
       "0          2  tv66ShUMWEDcg64hME6+9lvc4sPOHS/9HXrtTHmIzMEKPa...          16\n",
       "1          1  q+Z9iX2hCN0JA2hJIuIp9Bb/2wLoDfrfEv8LR5rSJjrPE0...          16\n",
       "2          1  q+aDOYNRCB52vD7EdADs5iUQKCj0LwoTa5uezQMcdKxRDJ...          16\n",
       "3          3  0yta0P��XAG2B2�n��zz4�7�Z�����o�J��8e�fu�7�o�g...          10\n",
       "4          2  kzNHyLC/RydeypLK50E/36+P49TxXHE90KWkM+mmE7cDib...          16\n",
       "...      ...                                                ...         ...\n",
       "19996      2  45Ar/qLwLA64Iu9togr3V0LDqeeSBUdLVdAPWqq7xhiATw...           5\n",
       "19997      2  PsfIoHptQh0HJbI7PSStBnxyIoUx5xrdtgxZuOxd14cwH5...          16\n",
       "19998      4                            r43763.tunnel.tuns.org.           5\n",
       "19999      1  q+aNi42jCMDVPbSPQtej99RiHkOiezYpVcsfplrVguvVjo...          16\n",
       "20000      2  XFDrQQGp/TPekj9Vol6lzOtmzsv1g6ndrO16TjCc905H9I...          16\n",
       "\n",
       "[20001 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.3 Jupyter notebook code — Drop duplicate rows in df_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       class                                               fqdn  query_type\n",
      "11525      1                         q+Z++38TBA.hidemyself.org.          16\n",
      "13770      1                         q+Z++38TBA.hidemyself.org.          16\n",
      "13715      1  q+Z+137vCKqKlonmeA7WUGsznkT4vkZtSDLL5eFC2a3B9v...          16\n",
      "18898      1  q+Z+137vCKqKlonmeA7WUGsznkT4vkZtSDLL5eFC2a3B9v...          16\n",
      "3723       1  q+Z+3373CEp8ZmqEPKY/DgW4+7oHuWos4YK6ZsBWiI5Xos...          16\n",
      "...      ...                                                ...         ...\n",
      "15568      4                            r49741.tunnel.tuns.org.           5\n",
      "11761      4                            r50821.tunnel.tuns.org.           5\n",
      "12522      4                            r50821.tunnel.tuns.org.           5\n",
      "18298      4                            r50947.tunnel.tuns.org.           5\n",
      "18649      4                            r50947.tunnel.tuns.org.           5\n",
      "\n",
      "[3288 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# In case you wanted to print the duplicates before we drop them\n",
    "# Sort the DataFrame by all columns to bring duplicates next to each other\n",
    "df_sorted = df_orig.sort_values(by=df_orig.columns.tolist())\n",
    "\n",
    "# Filter out the duplicates\n",
    "duplicates = df_sorted[df_sorted.duplicated(keep=False)]\n",
    "\n",
    "print(duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f13f432-39c1-4833-b98b-8121e0c1893c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orig = df_orig.drop_duplicates()\n",
    "df_orig = df_orig.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>fqdn</th>\n",
       "      <th>query_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>q+Z9iX2hCN0JA2hJIuIp9Bb/2wLoDfrfEv8LR5rSJjrPE0...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>q+aDOYNRCB52vD7EdADs5iUQKCj0LwoTa5uezQMcdKxRDJ...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0yta0P��XAG2B2�n��zz4�7�Z�����o�J��8e�fu�7�o�g...</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>kzNHyLC/RydeypLK50E/36+P49TxXHE90KWkM+mmE7cDib...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18352</th>\n",
       "      <td>1</td>\n",
       "      <td>q+Z/MX9JCFUnemD2OTRZh/+msL2EIE2/uGAw8Jc+8DxJm8...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18353</th>\n",
       "      <td>2</td>\n",
       "      <td>45Ar/qLwLA64Iu9togr3V0LDqeeSBUdLVdAPWqq7xhiATw...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18354</th>\n",
       "      <td>2</td>\n",
       "      <td>PsfIoHptQh0HJbI7PSStBnxyIoUx5xrdtgxZuOxd14cwH5...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18355</th>\n",
       "      <td>4</td>\n",
       "      <td>r43763.tunnel.tuns.org.</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18356</th>\n",
       "      <td>2</td>\n",
       "      <td>XFDrQQGp/TPekj9Vol6lzOtmzsv1g6ndrO16TjCc905H9I...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18357 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       class                                               fqdn  query_type\n",
       "0          2  tv66ShUMWEDcg64hME6+9lvc4sPOHS/9HXrtTHmIzMEKPa...          16\n",
       "1          1  q+Z9iX2hCN0JA2hJIuIp9Bb/2wLoDfrfEv8LR5rSJjrPE0...          16\n",
       "2          1  q+aDOYNRCB52vD7EdADs5iUQKCj0LwoTa5uezQMcdKxRDJ...          16\n",
       "3          3  0yta0P��XAG2B2�n��zz4�7�Z�����o�J��8e�fu�7�o�g...          10\n",
       "4          2  kzNHyLC/RydeypLK50E/36+P49TxXHE90KWkM+mmE7cDib...          16\n",
       "...      ...                                                ...         ...\n",
       "18352      1  q+Z/MX9JCFUnemD2OTRZh/+msL2EIE2/uGAw8Jc+8DxJm8...          16\n",
       "18353      2  45Ar/qLwLA64Iu9togr3V0LDqeeSBUdLVdAPWqq7xhiATw...           5\n",
       "18354      2  PsfIoHptQh0HJbI7PSStBnxyIoUx5xrdtgxZuOxd14cwH5...          16\n",
       "18355      4                            r43763.tunnel.tuns.org.           5\n",
       "18356      2  XFDrQQGp/TPekj9Vol6lzOtmzsv1g6ndrO16TjCc905H9I...          16\n",
       "\n",
       "[18357 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.4 Jupyter notebook code — Return the number of empty cells for every column in df_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dc7cc185-e1cd-49db-b2ed-58e4af04b47b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class         0\n",
       "fqdn          0\n",
       "query_type    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dfd6a808-3d43-416d-9e68-20e20197919d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class_counts = df_orig.groupby('class').size()\n",
    "plt.pie(class_counts, labels=class_counts.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.6 Jupyter notebook code — Extracting features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orig['fqdn_length'] = df_orig['fqdn'].str.len()\n",
    "df_orig['fqdn_count_numbers'] = df_orig['fqdn'].str.count('\\d')\n",
    "df_orig['fqdn_count_letters'] = df_orig['fqdn'].str.count('[a-zA-Z]')\n",
    "df_orig['fqdn_count_special'] = df_orig['fqdn'].str.count('[^\\d\\w\\s]')\n",
    "df_orig['fqdn_count_lower'] = df_orig['fqdn'].str.count('[a-z]')\n",
    "df_orig['fqdn_count_upper'] = df_orig['fqdn'].str.count('[A-Z]')\n",
    "df_orig['fqdn_count_num_fqdn_labels'] = df_orig['fqdn'].str.count('\\.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.7 Jupyter notebook code — Calculate the length of the first FQDN label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ffbb4fa8-6946-419f-9660-274e3634ce33",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def extract_labels(string):\n",
    "    return re.findall(\"[^.]+\", string)\n",
    "def firstlength(string):\n",
    "    return len(re.findall(\"[^.]+\", string)[0])\n",
    "df_orig['first_fqdn_label_length'] = \\\n",
    "    [firstlength(x) for x in df_orig['fqdn']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.8 Jupyter notebook code — Calculate the length of the maximum FQDN label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b43cb27-efbe-4693-af15-59ffacd28cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxlength(list):\n",
    "    return len(max(list, key=len))\n",
    "\n",
    "df_orig['extracted_fqdn_labels'] = [extract_labels(x) \\\n",
    "    for x in df_orig['fqdn']]\n",
    "df_orig['max_fqdn_label_length'] = [maxlength(x) \\\n",
    "    for x in df_orig['extracted_fqdn_labels']]\n",
    "df_orig = df_orig.drop(['extracted_fqdn_labels'], axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.9 Jupyter notebook code — Calculate the entropy of fqdn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from collections import Counter\n",
    "\n",
    "def entropy(s):\n",
    "    counts = Counter(s)\n",
    "    length = float(len(s))\n",
    "    entropy_value = 0.0\n",
    "    for count in counts.values():\n",
    "        probability = count / length\n",
    "        entropy_value -= probability * math.log(probability, 2)\n",
    "    \n",
    "    return entropy_value\n",
    "\n",
    "df_orig['fqdn_entropy'] = df_orig['fqdn'].apply(entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "89aeeccc-eb54-4f40-ba33-795ed97f3cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len('api.github.com')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2b3c8c40-3d63-489a-9f2b-fdf0d6edef78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.52164063634332"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy('api.github.com')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2381ef12-cce0-4f07-a9bc-4e7ed43ff60c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>fqdn</th>\n",
       "      <th>query_type</th>\n",
       "      <th>fqdn_length</th>\n",
       "      <th>fqdn_count_numbers</th>\n",
       "      <th>fqdn_count_letters</th>\n",
       "      <th>fqdn_count_special</th>\n",
       "      <th>fqdn_count_lower</th>\n",
       "      <th>fqdn_count_upper</th>\n",
       "      <th>fqdn_count_num_fqdn_labels</th>\n",
       "      <th>first_fqdn_label_length</th>\n",
       "      <th>max_fqdn_label_length</th>\n",
       "      <th>fqdn_entropy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>tv66ShUMWEDcg64hME6+9lvc4sPOHS/9HXrtTHmIzMEKPa...</td>\n",
       "      <td>16</td>\n",
       "      <td>244</td>\n",
       "      <td>36</td>\n",
       "      <td>192</td>\n",
       "      <td>16</td>\n",
       "      <td>107</td>\n",
       "      <td>85</td>\n",
       "      <td>11</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>5.788881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>q+Z9iX2hCN0JA2hJIuIp9Bb/2wLoDfrfEv8LR5rSJjrPE0...</td>\n",
       "      <td>16</td>\n",
       "      <td>194</td>\n",
       "      <td>35</td>\n",
       "      <td>151</td>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>5.810285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>q+aDOYNRCB52vD7EdADs5iUQKCj0LwoTa5uezQMcdKxRDJ...</td>\n",
       "      <td>16</td>\n",
       "      <td>194</td>\n",
       "      <td>20</td>\n",
       "      <td>165</td>\n",
       "      <td>9</td>\n",
       "      <td>84</td>\n",
       "      <td>81</td>\n",
       "      <td>5</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>5.771027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0yta0P��XAG2B2�n��zz4�7�Z�����o�J��8e�fu�7�o�g...</td>\n",
       "      <td>10</td>\n",
       "      <td>253</td>\n",
       "      <td>18</td>\n",
       "      <td>112</td>\n",
       "      <td>122</td>\n",
       "      <td>62</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>4.031804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>kzNHyLC/RydeypLK50E/36+P49TxXHE90KWkM+mmE7cDib...</td>\n",
       "      <td>16</td>\n",
       "      <td>245</td>\n",
       "      <td>34</td>\n",
       "      <td>192</td>\n",
       "      <td>19</td>\n",
       "      <td>103</td>\n",
       "      <td>89</td>\n",
       "      <td>11</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>5.784111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>r49669.tunnel.tuns.org.</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3.534219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>r23494.tunnel.tuns.org.</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3.621176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>U9pVkTmG1OhMuT2dEtzTTgTOXN9z9JxB35D3TFvaz8tuiL...</td>\n",
       "      <td>16</td>\n",
       "      <td>245</td>\n",
       "      <td>39</td>\n",
       "      <td>191</td>\n",
       "      <td>15</td>\n",
       "      <td>109</td>\n",
       "      <td>82</td>\n",
       "      <td>11</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>5.802774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>q+aCsILIBA.hidemyself.org.</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>4.286790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>r39774.tunnel.tuns.org.</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3.621176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class                                               fqdn  query_type  \\\n",
       "0      2  tv66ShUMWEDcg64hME6+9lvc4sPOHS/9HXrtTHmIzMEKPa...          16   \n",
       "1      1  q+Z9iX2hCN0JA2hJIuIp9Bb/2wLoDfrfEv8LR5rSJjrPE0...          16   \n",
       "2      1  q+aDOYNRCB52vD7EdADs5iUQKCj0LwoTa5uezQMcdKxRDJ...          16   \n",
       "3      3  0yta0P��XAG2B2�n��zz4�7�Z�����o�J��8e�fu�7�o�g...          10   \n",
       "4      2  kzNHyLC/RydeypLK50E/36+P49TxXHE90KWkM+mmE7cDib...          16   \n",
       "5      4                            r49669.tunnel.tuns.org.           5   \n",
       "6      4                            r23494.tunnel.tuns.org.           5   \n",
       "7      2  U9pVkTmG1OhMuT2dEtzTTgTOXN9z9JxB35D3TFvaz8tuiL...          16   \n",
       "8      1                         q+aCsILIBA.hidemyself.org.          16   \n",
       "9      4                            r39774.tunnel.tuns.org.           5   \n",
       "\n",
       "   fqdn_length  fqdn_count_numbers  fqdn_count_letters  fqdn_count_special  \\\n",
       "0          244                  36                 192                  16   \n",
       "1          194                  35                 151                   8   \n",
       "2          194                  20                 165                   9   \n",
       "3          253                  18                 112                 122   \n",
       "4          245                  34                 192                  19   \n",
       "5           23                   5                  14                   4   \n",
       "6           23                   5                  14                   4   \n",
       "7          245                  39                 191                  15   \n",
       "8           26                   0                  22                   4   \n",
       "9           23                   5                  14                   4   \n",
       "\n",
       "   fqdn_count_lower  fqdn_count_upper  fqdn_count_num_fqdn_labels  \\\n",
       "0               107                85                          11   \n",
       "1                80                71                           5   \n",
       "2                84                81                           5   \n",
       "3                62                50                           7   \n",
       "4               103                89                          11   \n",
       "5                14                 0                           4   \n",
       "6                14                 0                           4   \n",
       "7               109                82                          11   \n",
       "8                16                 6                           3   \n",
       "9                14                 0                           4   \n",
       "\n",
       "   first_fqdn_label_length  max_fqdn_label_length  fqdn_entropy  \n",
       "0                       63                     63      5.788881  \n",
       "1                       63                     63      5.810285  \n",
       "2                       63                     63      5.771027  \n",
       "3                       62                     62      4.031804  \n",
       "4                       63                     63      5.784111  \n",
       "5                        6                      6      3.534219  \n",
       "6                        6                      6      3.621176  \n",
       "7                       63                     63      5.802774  \n",
       "8                       10                     10      4.286790  \n",
       "9                        6                      6      3.621176  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.10 Jupyter notebook code — Select the features in df_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "af4724a6-c7f2-43a4-92be-b3acd01d9795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18357, 12)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig_selected = df_orig[['class', 'query_type',\\\n",
    "    'fqdn_length', 'fqdn_count_numbers',\\\n",
    "        'fqdn_count_letters', 'fqdn_count_special',\\\n",
    "            'fqdn_count_lower', 'fqdn_count_upper',\\\n",
    "                'fqdn_count_num_fqdn_labels', 'fqdn_entropy',\\\n",
    "                    'first_fqdn_label_length','max_fqdn_label_length']]\n",
    "\n",
    "df_orig_selected.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.11 Jupyter notebook code — Visualize the pairwise correlations between features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "194a3500-5076-4130-89cf-d78900e6793b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "sns.heatmap(df_orig_selected.corr(method='pearson'), annot=True, cmap='gray')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.12 Jupyter notebook code — Split df_orig_selected into df_orig_selected_train and df_orig_selected_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d1728d63-832b-4eee-9759-97ff0f8080a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df_orig_selected.drop('class', axis=1)\n",
    "y = df_orig_selected['class']\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.13 Jupyter notebook code — Check the shape of the training dataset, X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4172c166-f36a-4eb6-b9f7-61daf4cb8686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14685, 11)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.14 Jupyter notebook code — Generate a pie chart for the class distribution in X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "25d1b305-25af-4d87-ad5a-4a40c10c0146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_copy = X_train.copy()\n",
    "X_train_copy['class'] = y_train.values\n",
    "\n",
    "class_counts = X_train_copy.groupby('class').size()\n",
    "\n",
    "plt.pie(class_counts, labels=class_counts.index, autopct='%1.1f%%')\n",
    "plt.title('Class Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.15 Generate a pie chart for the class distribution in X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e5880d59-3c17-4059-8cb4-e9b294f22ea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_copy = X_test.copy()\n",
    "X_test_copy['class'] = y_test.values\n",
    "class_counts = X_test_copy.groupby('class').size()\n",
    "plt.pie(class_counts, labels=class_counts.index, autopct='%1.1f%%')\n",
    "plt.title('Class Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.16 Jupyter notebook code — Scale the features in X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f7667bb8-09b1-40f4-863d-cde9745076f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>query_type</th>\n",
       "      <th>fqdn_length</th>\n",
       "      <th>fqdn_count_numbers</th>\n",
       "      <th>fqdn_count_letters</th>\n",
       "      <th>fqdn_count_special</th>\n",
       "      <th>fqdn_count_lower</th>\n",
       "      <th>fqdn_count_upper</th>\n",
       "      <th>fqdn_count_num_fqdn_labels</th>\n",
       "      <th>fqdn_entropy</th>\n",
       "      <th>first_fqdn_label_length</th>\n",
       "      <th>max_fqdn_label_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.252260</td>\n",
       "      <td>-1.127754</td>\n",
       "      <td>-1.129351</td>\n",
       "      <td>-1.032688</td>\n",
       "      <td>-0.564747</td>\n",
       "      <td>-0.799379</td>\n",
       "      <td>-0.889782</td>\n",
       "      <td>-1.040091</td>\n",
       "      <td>-1.002396</td>\n",
       "      <td>-1.265427</td>\n",
       "      <td>-0.928237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.325585</td>\n",
       "      <td>1.028420</td>\n",
       "      <td>0.179983</td>\n",
       "      <td>0.309957</td>\n",
       "      <td>2.023944</td>\n",
       "      <td>0.447947</td>\n",
       "      <td>0.127682</td>\n",
       "      <td>0.345250</td>\n",
       "      <td>-0.190823</td>\n",
       "      <td>0.845125</td>\n",
       "      <td>0.843184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.252260</td>\n",
       "      <td>-1.156125</td>\n",
       "      <td>-1.129351</td>\n",
       "      <td>-1.072178</td>\n",
       "      <td>-0.564747</td>\n",
       "      <td>-0.880726</td>\n",
       "      <td>-0.889782</td>\n",
       "      <td>-0.693756</td>\n",
       "      <td>-1.108372</td>\n",
       "      <td>-1.193883</td>\n",
       "      <td>-1.229756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.377482</td>\n",
       "      <td>0.962221</td>\n",
       "      <td>1.364617</td>\n",
       "      <td>1.178727</td>\n",
       "      <td>-0.238019</td>\n",
       "      <td>1.451231</td>\n",
       "      <td>0.654582</td>\n",
       "      <td>1.730592</td>\n",
       "      <td>1.352042</td>\n",
       "      <td>0.880897</td>\n",
       "      <td>0.880874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.377482</td>\n",
       "      <td>0.479919</td>\n",
       "      <td>0.554078</td>\n",
       "      <td>0.704852</td>\n",
       "      <td>-0.288285</td>\n",
       "      <td>0.746220</td>\n",
       "      <td>0.472892</td>\n",
       "      <td>-0.347420</td>\n",
       "      <td>1.378014</td>\n",
       "      <td>0.880897</td>\n",
       "      <td>0.880874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14680</th>\n",
       "      <td>1.377482</td>\n",
       "      <td>0.962221</td>\n",
       "      <td>1.364617</td>\n",
       "      <td>1.191890</td>\n",
       "      <td>-0.263152</td>\n",
       "      <td>1.559694</td>\n",
       "      <td>0.600075</td>\n",
       "      <td>1.730592</td>\n",
       "      <td>1.320973</td>\n",
       "      <td>0.880897</td>\n",
       "      <td>0.880874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14681</th>\n",
       "      <td>-1.252260</td>\n",
       "      <td>-1.127754</td>\n",
       "      <td>-1.129351</td>\n",
       "      <td>-1.019525</td>\n",
       "      <td>-0.589880</td>\n",
       "      <td>-0.772263</td>\n",
       "      <td>-0.889782</td>\n",
       "      <td>-1.040091</td>\n",
       "      <td>-1.109628</td>\n",
       "      <td>-1.229655</td>\n",
       "      <td>-0.965927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14682</th>\n",
       "      <td>-1.252260</td>\n",
       "      <td>-1.071013</td>\n",
       "      <td>-1.129351</td>\n",
       "      <td>-0.940546</td>\n",
       "      <td>-0.589880</td>\n",
       "      <td>-0.609569</td>\n",
       "      <td>-0.889782</td>\n",
       "      <td>-1.040091</td>\n",
       "      <td>-0.523117</td>\n",
       "      <td>-1.265427</td>\n",
       "      <td>-0.702098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14683</th>\n",
       "      <td>1.377482</td>\n",
       "      <td>0.479919</td>\n",
       "      <td>0.865824</td>\n",
       "      <td>0.639036</td>\n",
       "      <td>-0.288285</td>\n",
       "      <td>0.637757</td>\n",
       "      <td>0.454723</td>\n",
       "      <td>-0.347420</td>\n",
       "      <td>1.379956</td>\n",
       "      <td>0.880897</td>\n",
       "      <td>0.880874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14684</th>\n",
       "      <td>1.377482</td>\n",
       "      <td>0.479919</td>\n",
       "      <td>0.491729</td>\n",
       "      <td>0.783831</td>\n",
       "      <td>-0.413950</td>\n",
       "      <td>0.827568</td>\n",
       "      <td>0.527399</td>\n",
       "      <td>-0.347420</td>\n",
       "      <td>1.366567</td>\n",
       "      <td>0.880897</td>\n",
       "      <td>0.880874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14685 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       query_type  fqdn_length  fqdn_count_numbers  fqdn_count_letters  \\\n",
       "0       -1.252260    -1.127754           -1.129351           -1.032688   \n",
       "1        0.325585     1.028420            0.179983            0.309957   \n",
       "2       -1.252260    -1.156125           -1.129351           -1.072178   \n",
       "3        1.377482     0.962221            1.364617            1.178727   \n",
       "4        1.377482     0.479919            0.554078            0.704852   \n",
       "...           ...          ...                 ...                 ...   \n",
       "14680    1.377482     0.962221            1.364617            1.191890   \n",
       "14681   -1.252260    -1.127754           -1.129351           -1.019525   \n",
       "14682   -1.252260    -1.071013           -1.129351           -0.940546   \n",
       "14683    1.377482     0.479919            0.865824            0.639036   \n",
       "14684    1.377482     0.479919            0.491729            0.783831   \n",
       "\n",
       "       fqdn_count_special  fqdn_count_lower  fqdn_count_upper  \\\n",
       "0               -0.564747         -0.799379         -0.889782   \n",
       "1                2.023944          0.447947          0.127682   \n",
       "2               -0.564747         -0.880726         -0.889782   \n",
       "3               -0.238019          1.451231          0.654582   \n",
       "4               -0.288285          0.746220          0.472892   \n",
       "...                   ...               ...               ...   \n",
       "14680           -0.263152          1.559694          0.600075   \n",
       "14681           -0.589880         -0.772263         -0.889782   \n",
       "14682           -0.589880         -0.609569         -0.889782   \n",
       "14683           -0.288285          0.637757          0.454723   \n",
       "14684           -0.413950          0.827568          0.527399   \n",
       "\n",
       "       fqdn_count_num_fqdn_labels  fqdn_entropy  first_fqdn_label_length  \\\n",
       "0                       -1.040091     -1.002396                -1.265427   \n",
       "1                        0.345250     -0.190823                 0.845125   \n",
       "2                       -0.693756     -1.108372                -1.193883   \n",
       "3                        1.730592      1.352042                 0.880897   \n",
       "4                       -0.347420      1.378014                 0.880897   \n",
       "...                           ...           ...                      ...   \n",
       "14680                    1.730592      1.320973                 0.880897   \n",
       "14681                   -1.040091     -1.109628                -1.229655   \n",
       "14682                   -1.040091     -0.523117                -1.265427   \n",
       "14683                   -0.347420      1.379956                 0.880897   \n",
       "14684                   -0.347420      1.366567                 0.880897   \n",
       "\n",
       "       max_fqdn_label_length  \n",
       "0                  -0.928237  \n",
       "1                   0.843184  \n",
       "2                  -1.229756  \n",
       "3                   0.880874  \n",
       "4                   0.880874  \n",
       "...                      ...  \n",
       "14680               0.880874  \n",
       "14681              -0.965927  \n",
       "14682              -0.702098  \n",
       "14683               0.880874  \n",
       "14684               0.880874  \n",
       "\n",
       "[14685 rows x 11 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#Scale the training set feature\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.17 Jupyter notebook code — Plot the PCA cumulative sum of the explained variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "72b64207-aa0f-45a1-9948-d41f30eb060a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Deterine the number of Principle Components\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "\n",
    "#fitting the pca algorithm with our data\n",
    "pca=PCA().fit(X_train_scaled)      \n",
    "#plotting the cumulative summation of the explained variance\n",
    "plt.figure()\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Variance % for each component')\n",
    "#plt.title('Explained variance')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.18 Jupyter notebook code — Apply PCA to X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "91bd0979-1b69-479e-b993-436d165faa9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=4)\n",
    "X_train_pca = pca.fit_transform(X_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "87df3741-4fc2-43db-86c0-6e1d69373d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            PC1       PC2       PC3       PC4\n",
      "0      3.316405 -0.222155 -0.347967 -0.333677\n",
      "1     -1.621158  2.052092 -0.545943  0.170106\n",
      "2      3.370213 -0.250137 -0.387346 -0.515230\n",
      "3     -3.618564 -0.539409  1.039047 -0.513343\n",
      "4     -2.124023 -0.367948  1.161952  0.936708\n",
      "...         ...       ...       ...       ...\n",
      "14680 -3.627216 -0.520639  1.094063 -0.587773\n",
      "14681  3.341135 -0.208918 -0.365088 -0.346860\n",
      "14682  2.983434 -0.334982 -0.181290 -0.384849\n",
      "14683 -2.169293 -0.423598  1.121481  0.933020\n",
      "14684 -2.151243 -0.472904  1.185491  0.928947\n",
      "\n",
      "[14685 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(pd.DataFrame(X_train_pca, columns=['PC1', 'PC2', 'PC3', 'PC4']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.19 Jupyter notebook code — Generate a plot showing PC1 and PC2 based on class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "60f5f4cf-554c-4138-977f-1aded4f8ff33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_pca_copy = pd.DataFrame(X_train_pca, \\\n",
    "    columns=['PC1', 'PC2', 'PC3', 'PC4'])\n",
    "X_train_pca_copy['class'] = y_train.values\n",
    "\n",
    "fig = plt.figure(figsize = (10,10))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('PC1', fontsize = 15)\n",
    "ax.set_ylabel('PC2', fontsize = 15)\n",
    "\n",
    "targets = X_train_pca_copy['class'].unique()\n",
    "colors = ['r', 'g', 'b', 'y', 'k']  # one color per class\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = X_train_pca_copy['class'] == target\n",
    "    ax.scatter(X_train_pca_copy.loc[indicesToKeep, 'PC1']\n",
    "               , X_train_pca_copy.loc[indicesToKeep, 'PC2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c122de4a-bc10-46a5-9e2b-f671dd726c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_pca_copy = pd.DataFrame(X_train_pca, \\\n",
    "    columns=['PC1', 'PC2', 'PC3', 'PC4'])\n",
    "X_train_pca_copy['class'] = y_train.values\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (10,10))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('PC3', fontsize = 15)\n",
    "ax.set_ylabel('PC4', fontsize = 15)\n",
    "\n",
    "targets = X_train_pca_copy['class'].unique()\n",
    "colors = ['r', 'g', 'b', 'y', 'k']  # one color per class\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = X_train_pca_copy['class'] == target\n",
    "    ax.scatter(X_train_pca_copy.loc[indicesToKeep, 'PC3']\n",
    "               , X_train_pca_copy.loc[indicesToKeep, 'PC4']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.20 Jupyter notebook code — Create and train a Random Forest Classifier model on X_train_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a45c995e-fb5f-4bf6-96fc-22b2832b84cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.21 Jupyter notebook code — Visualize a single decision tree in the Random Forest model rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v3//y5ctr166x2exPlkSboBaXxcfHr1ixgtf49OlTQkhmZqapqamA3lGjRgmYWURE5NChQ/wtxcXFR44cERERsbOzo1pWrlz55s2bGzduCD6CedGiRRUVFTt27GhNZsGpOu5ZNblvk8yCCX5WNTU1b9++7dWrl729fXR0NDVAT0/v9OnTI0aMaM1zbluq9jwrQoiPj09cXJydnd2oUaPExcUTEhLU1NSo01raOTMAdDcoLwIAAAAAQJejpKRkY2MTExNTXFysrKxMCAkPD+/du/eECRMIIcuXL/fx8eFtgG1oaJCTk2vNXmB+ZWVloaGhxsbGVG2RECIqKurl5RUfH3/x4kVPT0/+wSUlJRs2bGhpKl1dXcHlRX7r1q2rra0lhBgYGPTo0eOz43V0dIYPHx4XFxcUFOTk5MThcEJCQs6fP08IYbPZgntbGYkSHR3t4eFRUlKyf/9+Q0NDquXw4cMXL14UvC06MzMzIiJi7dq1P/zwQ2sytz7S131WgjN/kSbPKj09nRBy4MABbW3tQ4cOjRo16s6dO6tWrZo8eXJGRgb1F7ila79Wqi99VoQQOTm5Pn36pKenp6SkiIiIcDgcJpNZXV3d/pkBoLvB5mgAAAAAAOiK5syZw2azL126RAjJz8+/f/++q6urkJAQIURPT09NTW3Pnj3Tp08fPny4hobGs2fPvnT+rKwsLpdbU1PjxOf3338nhOTl5TUZrKenV9uyGTNmtP6+Hz58yM7OPnXqVGlpqYmJSVFRkeDxQkJCp06dUlFR8fLyUlFRUVFRWb58uZeXFyHEwMBAcG8rI+Xl5Tk4ONjb28vIyMTGxlL7oN++fevm5ubp6fnZA4IDAgJERUWXL1/eysytTEW+9rMSnLmVPvmsysrKCCH19fWRkZGLFy82MjLy8fFZvnx5cXFxeHi44Gu/Siry5c+KEDJ27NgrV64cPXq0qKiopKTkwoULVVVVkyZNys/Pb+fMANDdYPUiAAAAAAB0RaoL4fIAACAASURBVHZ2dvLy8pGRkfPnz6cOn507dy7VtWvXrvXr14uJiZmZmVlaWq5bt27Pnj3//vtva6alKkGEkNLSUkKImJgY/7vkevbsOWvWrOYlMAaD0Z51W/89WFPov8s7dHR0dHR0hISE5s6dGxMT4+7uLvhyQ0PDjIyMiIiIp0+fqqqqTpgwITExkfyvZCa497NCQkK8vb0ZDEZAQMCSJUvExMSo9t9+++39+/eVlZXUhnRCyOvXr7lcrpubm66uLm+T7KtXr0JDQ6dNm9ZkO3mbU3XosxKc+bNaela9e/cmhIwYMUJPT4832N7e3t/fn1f4buna9qRqz7N69uxZRkaGubm5t7c31TJ16tQ7d+7s3bv34sWLy5Yta8+fAgB0NygvAgAAAABAVyQmJubk5BQUFFRaWhoeHj5q1ChdXV1CSElJyerVq5WUlHJycqSlpanB1AvjmmMwGNQpLjxZWVnUBy0tLUKIjo5OSEgIr5fNZldXV0tISDSZp6ioaMuWLS1FdXd3HzZsmIDfsmPHjrVr1167ds3W1pbXqKioSP53xIcADQ0N//77r6KiooeHB/+EqqqqCgoKgnsFz0wIiY6OdnV1HTlyZFhYWJMNuUpKSkOGDMnJyeG11NfXczic9PR0XtWJEBIYGNjY2Mh/989mFhyp456V4MyfJeBZUV+bHKlcV1dHCJGVlRV8bXtStedZPX78mBBiZmbG3zhhwoS9e/eWl5e3Z2YA6IawORoAAAAAALqoOXPmNDY27ty5Mz09nbeG7uXLlxwOZ+rUqbzaYkFBAfXyu+Y0NTXz8/N5dZ/MzMzc3Fzqs7a2tpKS0o0bN/irQtu3b5eXl09OTm4yT0VFRVDLmm+mboJ6xd7Nmzf5G0+cOEEIGTx4sOBra2tr9fT0fHx8eC2FhYUXLlxwcHD4bO9nrV27VlZWNjIysnnNy8fHJ+3/0tfX19LSSktLO3nyJG9YbGysgoLC+PHjW59ZsI57VoIzf5aAZ9WjRw8LC4sHDx7wV2MvX75MCKHOkxFwbXtStedZDRgwgBASGRnJ3xgREUFN256ZAaA74gIAAAAAAHSuGTNmzJgxozUjdXV1hYWFJSQkKisrqZaqqiopKSkFBYWrV69mZ2efPn1aXV1dXl5eRkbm+fPnXC7XzMxMXV2dGrx582ZCiIuLS0JCwokTJ7S1takVWPX19VwuNygoiBDi6OhIFYZ2794tJiY2YcIEDofT/t84YMCAiRMnUp/ZbLahoaGwsPDGjRvv3r0bGRnp5OQkJCRkbGzMYrH4r6LqU56envyNFhYWIiIiJ0+eLCsrS05OHjJkSN++fauqqlrTu3fvXmFh4U2bNjVPWFZWxmAwjIyMVjQTFRXVfLyRkZG+vn6TGYSEhCZPntx8cJtTdeizEpy5Pc8qOTmZwWAMGTIkJiYmIyPjwIED4uLiY8aMaeVzbluq9jwrNpttZWVFCLG2tv7jjz/+/vvv5cuXi4uLGxgY1NfXt2Zm6oD15qmaoN5s8NlhAPBNw//IAQAAAACgs7W+vEhtSZ49ezZ/Y0REhJSUFLVgQkFB4ffff4+MjJSUlGQymdz/W1788OGDjY0NNbJ3796rV69evXo1r7zI5XIPHjwoLi5ODWAymQsWLCgtLf0qv5G/vMjlcvPy8kaOHMm/1GPq1KmFhYVNrvpkySw/P5//WiMjo7t377ayd/fu3YSQjRs3Nk8YFRXV0jKU//znP83HNy8vXrhwgRCyY8eO5oPbnKpDn5XgzO18VjExMb169eK1Ozg4UH+XWnNt21K181mVlpa6uLgwGAzetaamprm5ua2cGeVFAOBhcLnclv6fDgAAAAAAoCM4OjqS/+3EbJvS0tK0tDRVVdUBAwZQ9ZHS0tLy8nJtbe3mg0tKSl6/fj148GD+SgpPdXV1WlpaTU2NoaGhhoZGmyM1YWBg8MMPP1y/fp3XwuFw/v333+fPn/fo0aN///7UeSCtxOVyMzIyXrx4YWRk1HyDreBef39/LS0tZ2fnNv+WtmlPqo57VoK181mxWKwnT56UlJQYGhqqqqq2bZIvTdWeZ0UIKSwszMzMrKur09PT69+/P///RgTPPGfOnOjoaOqIJAEiIiKcnJxQeQD4vqG8CAAAAAAAna395cWur3l5kRa5ubnW1taJiYlfsXLafkjVel0zFUF5EQD44GgXAAAAAACADpGenu7o6Lhv3z4aM+Tl5UVFRXW1yhRStV4XTHX69GlHR8ekpCS6gwBAV8GkOwAAAAAAAMB3yMrKqqCggDolhsYY1tbWNN69JUjVel0wFZfL5XA4w4cP553eDgDdHMqLAAAAAAAAXx+9ixYBOo67u7u7uzvdKQCgC8HmaAAAAAAAAAAAAGgjlBcBAAAAAADg02JiYsLDw+lO0SocDofuCAAA3RTKiwAAAAAAAPBpAQEBK1eupDuFINnZ2b6+vpqamj179rSzs4uLi6M7EQBAt4N3LwIAAAAAAMA3qa6uzsHB4fXr1y4uLj179rxw4YK9vf2ff/5pampKdzQAgG4E5UUAAAAAAAAghBDqkGsGg0F3kNZat25dVlZWTEyMjY0NIcTX13fw4MFz58598eIF3dEAALoRbI4GAAAAAADo7h49emRpaSknJychIWFiYnL9+vVPDktMTFy0aJGurq6Ghoazs/OxY8fYbDbV9fHjxw0bNvTr109MTExHR2f+/PnV1dWf7Wqn4ODgQYMGUbVFQkivXr2sra3//fff+/fvf5X5AQCgNbB6EQAAAAAAoFtLTEy0sbHp2bOnh4dHZWXlhQsXHBwckpKSRo0axT8sISFhwoQJsrKyLi4uioqKN2/e9Pb2fvHiRUBAACFk4cKFZ86c+fnnn4cOHZqXl3fixImMjIx//vlHcFd7vH//vry83M3Njb9RV1eXEJKammpiYtLO+QEAoJVQXgQAAAAAgM4mLi5eWVlJdwoghBAOh7N06VIxMbHExERtbW1CyMqVKwcMGHD06NEm5cWwsDAmk5mXlycnJ0cIWbVqlZaWVlRUVEBAQH19fUhIyKRJk06fPk0N7tevn6+vb3Z2dp8+fVrqokqBPO/fvz969GhLOadNm2ZgYMDfkpWVRQhRVVXlb+zfvz8hpLi4uK3PA76yurq6Hj160J0CADoWyosAAAAAANDZFBQUcnNz6U4BhBCSlpb26NGjOXPmULVFQoient7Bgwc5HE6TkcuXL/fx8aFqi4SQhoYGOTk5qkxMbZFOTExMS0sbOnQoIWTx4sUeHh7i4uL19fUtdTWZv6SkZMOGDS3l1NXVbVJepP4KKSgo8Df26dOHEFJRUfGFjwE6SmlpaZM/IwD4/uDdiwAAAAAA0Nn09fUzMzOpg0SAXlSRztDQkL9x8eLFS5YsaTJST09PTU1tz54906dPHz58uIaGxrNnz6guCQmJDRs2VFVVGRkZDRgwYNGiRdevXxcTExMWFhbQ1Xz+2pbNmDGjyXgxMTFCSFlZGX/jhw8fCCHy8vLtfS7wlTx58kRfX5/uFADQsVBeBAAAAACAzjZixIiqqqrU1FS6gwApKSkhhPTu3fuzI3ft2qWurr5lyxYWi2VpaRkcHDx69Ghe77p163Jzc/38/CQkJI4dO2ZnZ2dgYFBUVCS4ix+DwejRsublSBUVFUJIk0OiqWqjkpJSW54FdICEhIQRI0bQnQIAOhY2RwMAAAAAQGcbNGjQDz/8cOHCBWNjY7qzdHeampqEkPv378+cOZPXeObMGQ6HM3fuXF5LSUnJ6tWrlZSUcnJypKWlqUZ/f3/qQ0NDQ21traam5ubNmzdv3lxUVOTv73/48OFDhw5t2LChpS7e5ZSioqItW7a0lNPd3X3YsGH8Lbq6ugwGo0l58dGjR4QQnOvSRaSkpOTn59vb29MdBAA6FlYvAgAAAABAZ2MwGG5ubqdPn66traU7S3dnbGzco0eP+Ph4XsvTp0/nzp2blJTEP+zly5ccDmfq1Km82mJBQUF6ejr1OT4+Xl5ePiwsjPqqoqKycuVKQkh5ebmAriZJKioqglqWl5fXZLyampqpqemtW7d4XSwWKzQ0tHfv3k0KkUCXo0ePDhw48Mcff6Q7CAB0LJQXAQAAAACABgsXLqyvr9+xYwfdQbq7Xr16LV269PHjxwsWLEhNTT1z5oyzszOTyVywYAH/sP79+0tJSZ07dy4qKionJyc4OHjUqFEyMjI1NTVZWVmjR49WVlbevHlzYmJiZWXlgwcPli5dSgiZNGmSgK4mSfT09Opb5ujo2Dz82rVrWSyWo6PjxYsXExIS7O3tX7x4ceLECQaD0WEPDForPT39jz/+WLVqFd1BAKDDMfA2ZQAAAAAAoMXevXvXrVv39OnTvn370p2lW2Oz2b/++uuuXbuoA6BVVVUPHDhAHaVibm6el5dXUFBACDl//ry7u3tNTQ0hREFBYd++fZKSknPmzKmvr2exWH/99decOXPevHlDzSkuLu7n57d27VpCiICu9gsPD/f09KROdJGTk9u4caOvr+9XmRnaydzcvKGh4c6dO6j2Anz3UF4EAAAAAAB6sFisQYMGqamp/fnnnyIiInTH6e4+fPjw+PFjGRkZHR0dUVHRT44pLS1NS0tTVVUdMGAAVTMqLS0tLy/X1tYmhNTW1j5+/PjVq1eKiooDBw5UVlbmXSigq/0aGxtTU1M5HI6JiUnzE2CAFgcPHly2bFlycjI2qgN0BygvAgAAAAAAbTIzM0eNGjV9+vSTJ0/SnQUAvo4bN27Y2dlt2bJl9erVdGcBgM6Ady8CAAAAAABtDAwMQkJCgoODt27dSncWAPgKkpOTHR0dZ8+ejdoiQPeB8iIAAAAAANDJ3t7+8OHDGzdunDdvHovFojsOALRdZGTkuHHjxowZc/z4cbqzAEDnQXkRAAAAAABo5u3tfenSpbCwsIkTJ+bn59MdBwC+2MePH9evX+/o6Ojh4XH16tWWXt8JAN8llBcBAAAAAIB+9vb2t2/ffvPmzYABA9avX19bW0t3IgBorUuXLhkYGOzbt+/o0aMHDx7EATsA3Q2OdgEAAAAAgK6CxWIdOnRo8+bNoqKibm5u06dPHz58OHVCMQB0NYWFhVevXg0KCkpPT581a9bOnTvV1NToDgUANEB5EQAAAAAAupbi4uLffvvt1KlTr169kpGRMTAwUFRUFBcXpzsXABBCCJvNLi8vz8nJKSwslJSUnDZtmo+Pz/Dhw+nOBQC0QXkRAAAAAAC6qEePHt27d+/p06fl5eUfP36kO8637cOHD48fPx42bFh3fileZWXls2fPjI2NsXu3PYSEhOTk5LS0tIyMjMaMGYPSPwCgvAgAAAAAAPCde/36tZmZmaSkZEJCgoKCAt1xaJORkWFhYTF06NCrV6+iKAYA8LWgvAgAAAAAAPA9KykpMTc3Z7PZSUlJvXr1ojsOzR49emRhYWFsbHzlyhUxMTG64wAAfA9wcjQAAAAAAMB3q6KiYuLEiQ0NDfHx8agtEkIGDx588+bN5ORkZ2dnFotFdxwAgO8ByosAAAAAAADfp6qqKisrq5KSkps3b+JIXx4jI6Nr167dvHnTxcWlsbGR7jgAAN88lBcBAAAAAAC+Q7W1tfb29m/evElISNDU1KQ7TtcycuTI69evX79+3cPDg8Ph0B0HAODbxqQ7AAAAAAAAAHxl9fX106ZNe/bsWWJiYr9+/eiO0xWNGTPm0qVLDg4OwsLCQUFBQkJYfAMA0EYoLwIAAAAAALTKzZs3X716JWDAtGnT5OTkOi1PSxoaGqZPn37v3r24uLgBAwbQHafrmjBhwqVLl6ZMmSIpKXnw4EEGg0F3IgCAbxLKiwAAAAAAAK1y6NChqKgoAQNGjhxJe3mRzWb//PPPt27d+uuvv4yMjOgN0/VNnDgxLCzM0dGRyWTu27eP7jgAAN8kBpfLpTsDAAAAAADANyA3N7eiooL6nJ2dPWvWLCsrK39/f96AgQMHiouL05SOEEI4HI6rq+ulS5euX79uampKY5JvS2RkpLOzs6+v7+7du+nOAgDw7cHqRQAAAAAAgFbR1tbmfRYRESGEKCgoDB8+nL5E/weXy/X29j5//vyVK1dQW/wi06dPr62tdXNzk5WV9fPzozsOAMA3Bi+vBQAAAAAA+GqWLFni4eFRWFi4aNEiJSUlQoirq+vs2bP5x+zYsWPs2LGNjY3U14qKioULFw4cOFBFRWXq1KkxMTFtu/XKlStPnz594cKFiRMntvNXdEOurq4nTpzYuHHj9u3b6c4CAPCNwepFAAAAAACAr+bx48dFRUWTJk16/Pgx9erDBw8ecDgc/jE5OTm3b9+mGgsLC8eOHVtSUuLq6iorK3vjxg17e/s9e/YsXbr0i+67Zs2a/fv3nz171s7O7iv+nG7F3d29trbWx8eHyWSuXLmS7jgAAN8MlBcBAAAAAAC+pqysLGtr63Pnzunp6X128OrVq/Pz8+/du2diYkII2bRpk42NzapVq1xdXRUUFFp5x40bNwYEBJw5c8bJyald0bu9xYsXNzY2Ll++XFJScuHChXTHAQD4NmBzNAAAAAAAwFe2ZcuW1tQWy8rKQkNDjY2NqdoiIURUVNTLy6uhoeHixYutvNf+/fs3b9589OjRWbNmtT0x/M/SpUt37dq1ePHiwMBAurMAAHwbsHoRAAAAAADga1JSUjI2Nm7NyKysLC6XW1NTw7/qsKqqihCSl5fXmhkOHz68bNmy3bt3z58/v21pobkVK1ZUVFR4e3tLSEg0eW8mAAA0h/IiAAAAAADA1yQmJiZ4QFlZGfWhtLSUGk+dQ03p2bPnrFmzDAwMPnuj4OBgX1/f7du3r1ixoh154RO2bNnS2Ng4d+5cJpM5c+ZMuuMAAHRpKC8CAAAAAAB0IAaD0eRol6ysLOqDlpYWIURHRyckJITXy2azq6urJSQkBE8bGRnp6enp5+e3evXqrx0ZCCFk+/btbDbb1dVVQkLCwcGB7jgAAF0XyosAAAAAAAAdSFNT8+bNmywWi1qimJmZmZubS3Vpa2srKSnduHGD10sI2b59u5+f399//z1mzJiW5rx8+bKzs7OPj8/GjRs7/hd0Xzt37qypqZkxY8bFixcnTZr0Rdc+f/78zp07n+ySlJT87IrImJiYqqoqLJwEgG8CyosAAAAAAAAdyMTE5Nq1a3PnzvXy8srNzd25c6esrOz79+8JIaKiotu3b/f09Jw9e/aqVatkZGSuXLmydevWCRMmjB49uqUJY2NjZ86cOX/+/H379nXi7+iOGAzGkSNH2Gz2tGnTLl26ZGNj0/prk5KSFixY8MkuDQ2Nz9YNAwIC8vLyUF4EgG8CyosAAAAAAAAdaMWKFXfv3g0NDQ0NDe3du/fPP/9MCNmxYwfV6+HhUVtb+8svv0RERBBCmEymp6env78/g8H45Gzx8fGTJ0+eOXPmwYMHO+0ndGcMBuO333778OHD9OnTr127Zm5u/kWXL1u2rPnGanFx8a+WDwCgC2BwuVy6MwAAAAAAAHznSkpKXr9+PXjw4E/WDaurq9PS0mpqagwNDTU0NFqa5O7du1ZWVhMnTgwPDxcWFu7IvPB/sNns2bNnR0VFXb9+fezYsa255Pjx4wsWLPjtt99aWsMomLm5eV5eXkFBQRuuBQDoZEJ0BwAAAAAAAPj+KSkpDRkypKU1idLS0qampra2tgJqi+np6ZMmTbK0tAwLC0NtsZMJCwufOXNm/Pjx9vb2KSkpX2vaxMTERYsW6erqamhoODs7Hzt2jM1mNx/28ePHDRs29OvXT0xMTEdHZ/78+dXV1bzeioqKhQsXDhw4UEVFZerUqTExMV8rHgBAK6G8CAAAAAAA0NVlZGRYWloaGxuHh4czmXjJFQ1ERETOnz8/ZswYKyurhw8ftn/ChIQES0vL8PBwa2trT0/PgoICb2/vNWvWNB+5cOFCf39/U1PTXbt22dra/vHHH9bW1lRXYWHh0KFDz5w5Y2pq6ubmlp+fb29vv3///vbHAwBoPWyOBgAAAAAA6NKys7PNzMwGDBgQHR3do0cPuuN0a3V1dXZ2dhkZGQkJCQYGBgJGUpujBw8e3LdvX/52JpN5/vx5Qsi8efPOnDlTVFQkJydHCPn48aOWlpasrOyzZ88I3+bo+vp6aWlpGxubK1euUDMcPHjQ19c3KytLV1d39uzZZ8+evXfvnomJCSGkoaHBxsbm9u3bb9++VVBQ6KCHAADQBMqLAAAAAAAAXdfLly9NTU179+4dGxsrJSVFdxwgtbW1kyZNevr0aWJior6+fkvDqPKitLR0k4qwiIhIYWEhIeT58+csFsvQ0JBqr6qqGjFiRGVl5evXrwlfebG2tlZOTq5Hjx6JiYlDhw4lhHA4nLq6OnFx8crKSkVFxeHDhycnJ/PmDw8Pd3Z2PnHihKenZ0f8fACA5rCoHgAAAAAAoIsqLCwcN26cgoLCtWvXUFvsIiQkJKKjo21sbKysrJKSkrS0tAQMDggIaOloFz09vdLS0j179ty9ezc/Pz8nJ6eqqkpNTa357TZs2PDrr78aGRnp6+uPGzfO1tbW2tpaWFg4KyuLy+XW1NQ4OTnxxldVVRFC8vLy2v1DAQBaC+9eBAAAAAAA6IqKi4snTJggJSX1119/ycvL0x0H/j9JScmoqChVVdVx48bl5+e3bZJdu3apq6tv2bKFxWJZWloGBwePHj36kyPXrVuXm5vr5+cnISFx7NgxOzs7AwODoqKi0tJSQoiYmJgIn549e86aNUvwxm0AgK8LqxcBAAAAAAC6nPfv31tYWLDZ7ISEhJ49e9IdB5qSlZWNjY0dP378hAkTkpKSmq86FKykpGT16tVKSko5OTnS0tJUo7+/f/ORDQ0NtbW1mpqamzdv3rx5c1FRkb+//+HDhw8dOjRr1ixCiI6OTkhICG88m82urq6WkJBox48DAPgyWL0IAAAAAADQtVRWVk6cOLG6uvrmzZsqKip0x4FPk5OT+/PPP8XExMaNG/f27dsvuvbly5ccDmfq1Km82mJBQUF6enrzkfHx8fLy8mFhYdRXFRWVlStXEkLKy8u1tbWVlJRu3LjBYrF447dv3y4vL8//NkYAgI6G8iIAAAAAAEAX8uHDBzs7u3fv3iUmJvbp04fuOCCIkpJSfHy8sLCwhYXFu3fvWn9h//79paSkzp07FxUVlZOTExwcPGrUKBkZmZqamqysLP6Ro0ePVlZW3rx5c2JiYmVl5YMHD5YuXUoImTRpkqio6Pbt26uqqmbPnv3w4cPc3Nw9e/Zs3bp1woQJLe2zBgDoCNgcDQAAAAAAQAMul8tgMJo01tbW2tnZZWdnJyUl9e3bl5Zg8EWUlZVjY2PNzMysra3j4+MVFBRac5W0tPSpU6fc3d0dHBwIIQoKCvv27ZOUlJwzZ87AgQP5VyNKS0ufPXt2zpw548aNo1rExcX9/f0nTZpECPHw8Kitrf3ll18iIiIIIUwm09PT09/fv/lfLQCAjsPgcrl0ZwAAAAAAAOh2Ll68mJycvGPHDl5LQ0PDlClTUlJSEhMTcTTHt+XVq1dmZmby8vJxcXGtP4entLQ0LS1NVVV1wIABVEGwtLSU2vXcZGRtbe3jx49fvXqlqKg4cOBAZWVl/t7q6uq0tLSamhpDQ0MNDY2v8osAAFoP5UUAAAAAAAAaGBsbp6am+vr67tu3j8FgsFisadOm3bp1Ky4ubtiwYXSngy+Wm5trZmbWp0+fGzdu8N6oCADQHeDdiwAAAAAAAJ3t9u3bqamphJBDhw7NmzePxWK5urrGx8dHR0ejtviN0tbWTkhIyM/Pt7Gx+fDhA91xAAA6D1YvAgAAAAAAdDY7O7vY2FjqFXtCQkKamppv376NiYkxNzenOxq0S0ZGhoWFxZAhQ6KiosTFxemOAwDQGVBeBAAAAAAA6FRZWVn6+vr8/xZjMBg//vhjUlKSmJgYjcHgq3j06JGFhYWxsfGVK1fwBwoA3QE2RwMAAAAAAHSqXbt2MZlM/hYul/vgwYPJkyfX1dXRlQq+lsGDB//111/JyckzZ87kPwMaAOB7hdWLAAAAAAAAnefdu3caGhqfrDoxmcyxY8dGR0dLSEh0fjD4uu7evWttbW1lZRUeHt6kmgwA8J3B6kUAAAAAAIDOc+jQoZa6GhsbExISXF1dOzMPdJCRI0fGxMT8+eefHh4eHA6H7jgAAB0IqxcBAAAAAAA6yYcPH9TU1Kqqqpp3MZlMCQmJZcuWLV++XEZGpvOzQUf466+/7O3tnZ2dg4KChISwvgcAvk9YoQ0AAAAAANBJgoKCPnz40KSRyWSKior6+PisWbNGVlaWlmDQQSwtLS9fvjx58mRhYeHAwEAGg0F3IgCArw+rFwEAAAAAADoDm83u27dvYWEh719hIiIiTCZzyZIlq1evlpOTozcedJxLly45OTktXLhw//79/O3FxcVPnz41NzenKRcAwNeB1YsAAAAAAACdISIigldbFBERERYW9vX1XbVqlby8PN3RoGP99NNPoaGhzs7OTCZz9+7dVOPbt29NTU3FxMSePHlCbzwAgHZCeREAAAAAAKAzbN++nRDCZDKZTOby5ctXrFihoKBAdyjoJNOnT6+trXVzc5OVlfXz8ysoKDA1NS0sLGxsbIyNjbWysqI7IABA26G8CAAAAAAA0OHi4+MzMjLExcWXLFmycuVKRUVFuhNBZ3N1dWWz2Z6enjU1NeHh4W/evGlsbBQWFt62bRvKiwDwTcO7FwEAAAAAADrctGnT+vbt+8svvygrK9OdBei0adOmrVu3MhgMFovFa7x3756JiQmNqQAA2gPlRQAAAAAAQdhs9v3791NSUrKzsysqKvgrAgCtxOVy6+vrxcXF6Q7y7REREZGTk9PV1TU2NjYxMREWFqY7UbtkZWWZmpqWlZU1NjbyGkVEROzt7S9cuEBjMACAqfM4hgAAIABJREFU9kB5EQAAAADg03Jzcw8dOnT27NnS0lIlJSV9fX0FBQURERG6cwF0IywWq6ys7NmzZyUlJT179pw1a5aPj4+2tjbdudri2bNnpqamFRUV/LVFipCQ0PPnz3V0dGgJBgDQTigvAgAAAAA0VVVVtXXr1gMHDqirq3t5ef3000/9+/enOxRAt5aVlXXp0qUTJ04UFhb6+vr++uuvMjIydIf6Ag8fPrSwsKipqWGz2c17RURE3N3djx071vnBAADaD+VFAAAAAID/IyYmxsPDg8VibdmyZd68ed/6ZkyA7wmbzQ4MDPTz8xMRETl58qStrS3diVorKSlp5cqVKSkpTCaz+epFQoioqOirV6969erV+dkAANpJiO4AAAAAAABdyIEDBxwcHKysrLKzs729vVFbBOhShIWFvb29s7OzraysHBwcDhw4QHei1jIzM0tOTv77779HjBhBCGEymU0GcLncQ4cO0RENAKC9sHoRAAAAAOC/fH19Dx8+vH379l9++YXuLADwGQEBAWvWrFm8ePE3VGSk3L59e82aNbdv326yklFaWvr169fS0tI0ZgMAaAPhjRs30p0BAAAAAIB+AQEBO3bsOHfunJeXF91ZAODzRo8ebWBgsGrVKnFx8dGjR9Md5wv88MMP7u7ulpaWWVlZr169YjKZHA6HEMJms3v27Dly5Ei6AwIAfBmsXgQAAAAAINeuXZs8efKePXt8fX3pzgIAX2D//v0rVqy4cOHClClT6M7SFrGxsb/++ivvnYwqKiqvXr3CCfUA8G1BeREAAAAAurvi4mJdXV1nZ+fffvuN7iwA8MW8vb3DwsKys7OVlZXpztJGvCIjISQ4OHjOnDl0JwIA+AIoLwIAAABAd+fh4REbG/v8+XNJSUm6swDAF6urq9PX17e0tAwKCqI7S7vExsb6+fl9+PAhIyODwWDQHQcAoLVQXgQAAACAbi01NdXExCQ0NNTJyYnuLN+858+f37lzh79FWFhYU1Nz2LBhzU+roE7Rffjw4YcPH3R1dc3NzW1tbTsx7KfV1NRISUm1ZiSXy62oqJCXl2/zvaqrqxsaGnr27NnmGYDn3LlzLi4u9+/fHz58eKfd9NGjR/fu3cvMzCwvL6+vr/9a0757905KSgr/taM5ISEhOTk5LS0tIyOjMWPGiIuL050IAP4L5UUAAAAA6NYcHR1fvXp19+5drBVqv+PHjy9YsKB5u5KSUmBgIO/VeI2NjWvWrNm9ezchRF5enqrTEUImTpwYGhravGCno6Njbm5+4sSJjkv+8OHDNWvWpKSklJeX9+rVa/Lkybt27ZKRkfnk4PLy8l9++eXs2bN1dXXS0tI2NjZHjhxRVFT8otilpaWDBg2SlZV9+vTpV/4x3RKXyx05cuQPP/wQERHR0fcqLi7+7bffAgNPvnlTICoqLSWlJ8SQI0Sso+8LVFX/Y33+hw+ve/SQnDZtqq/vks4sKANAS5h0BwAAAAAAoE1ZWdnly5dPnjyJ2uJX5Ofnx1sKWlJSkpycvH79+tmzZz9+/FhLS4sQ4ujoeOnSJXNz82PHjvXv37+xsfHOnTtBQUEhISGurq5Xr17l/+MIDg7Ozc01NzfvuMCpqamWlpZMJtPFxUVBQeHcuXOBgYFpaWn37t0TEhJqMrihocHW1vb+/fvu7u4jR45MTk4ODAwsLCxssmzzs7E9PDzevHkjKyvbEb+oG2IwGIsWLfp/7N1nXFTX+jbgZxp96B1RAUGQUBQUG4iIBQU0GkWTWBCNsaBHjW+OmuRojMYkxm4S1KPEflSMCqIRwaCSCHalKCCgNOmMlBlgyvthzp9DUAdEYFPu65cPzFp7rX0vtiHxYe+9goKCSktLdXV12+gsdXV1u3bt+te/vpZIeHq6Ac4OEzQ0HInw06O91dTml5ZFRYQfO3p00IwZH/3ww3empqZMhwLo1lBeBAAAAIDu6/fffyeiiRMnMh2kSzE1NbW3t6//6OnpKZVKV69eHR4evmzZspiYmN9++23kyJFXrlyRF++4XO6IESOGDh16+/btiIiIGzduuLu75+TkrF+//tatWw8ePGjrwLt37xYKhfHx8c7OzkT09ddfe3t7R0dHh4WFTZ06tdHBv/76682bN7ds2bJy5UoiCgoKYrFYISEht2/fdnV1bWbsn3/++dKlS21XBeue/P39iej333+fMWNGW8z/4MGDDz6YnpWVZWL8qbnZEjZbtS3OAs2hrGRiYjTLxGhWSenF8+c3/PZb361bf3jtrdMA0D4a/y4OAAAAAKD7+Ouvv1xcXN70DCy0FhsbGyJ6/vw5EW3YsIGINm3a1OjGQB6Pt3fv3lmzZskflK6oqEhNTdXS0ho4cGDLTlpQULBnzx75VryK/fnnn87OzvLaolxgYCARJSQkvHrwkSNHDA0Ng4OD61vWrFlz6NAhAwODZsZOSkpauXLld999Z2Ji8lYrAsW0tLRcXFxu3rzZFpOHh4cPGTK8tFTf2fGPXuarUFvsIPR0fRztr+rrzVu0aFFw8FKJRMJ0IoBuCncvAgAAAED3lZKS0vA+O2gjx48fJyIrKysiunfvnqGh4eDBg189zN3d3d3dXf61nZ1dbGwsEaWnp1tbWzf/XMXFxWFhYSdPnoyNjZVIJGFhYYoLlHV1dWPHjh00aFDDxuzsbCJ67d2FaWlpPj4+SkpKGRkZiYmJZmZmTk5OM2fObGZskUg0Y8YMd3f3pUuXtunbJLsne3v7lJSUVp/2559/XrIk2MhwumXvTSwW/hLdsbDZyr3MV6mr9Qv5ZVla2tPz539TUlJiOhRAt4OfjAAAAADQfRUXF7f45jh4k+zs7Hv37sm/zsrKio6OPnv2rI6OTkBAQFFRkUAgaFTLaxVlZWVnzpw5efJkTEyMWCx2cnJau3btxIkTBwwYoHggj8fbtWtXw5bCwsI9e/bweDxfX99GB1dWVubn5xsZGfn5+UVERMgbbW1tDx48+NqC6atWrVqVl5f3+++/43WfbcHAwKD+z15rCQ8PX7x4Sc8en5n3WNa6M0Mr0teboKxsdvXq9PnzF/z660Gm4wB0OygvAgAAAED3VVtbi/tcWt2mTZs2bdrUsMXMzOzIkSN6enrPnj0jIkNDw1Y8XUFBwdy5c6OiomQymYeHx48//jhx4sRevXq99mCpVCoUCus/qqiocDichgdEREQEBQUVFRVt377dwcGh0fD09HQi2rFjR58+fXbt2jV06NC4uLjPP/984sSJjx49anJdERERu3fvPnPmDB6LbiPKysoikagVJ0xKSpox42Mjw2moLXZ8fA1na6tfjhyZbWfX95///CfTcQC6F5QXAQAAAACgNQUFBY0ZM0b+NYfDsbS0tLOzU1FRISJzc3MVFZW8vLxWPF1hYWFkZCSXy12yZElgYKCjo6OCg+Pj44cOHVr/8dixY/XbgDx9+nT58uXh4eF9+vQ5evSot7f3q8NLS0uJqKam5vTp07a2tkQ0YMCAgoKCjRs3njhxYunSpQpOnZ+fHxgYOG/evPfff78Fy4T2V1dXN2nSB8rKjlYW3zGdBZpFR9uzd8+v1qxZO3r0aBcXF6bjAHQjKC8CAAAAAEBrcnV1nTZt2mu72Gy2tbX106dP6+rqeDxeo964uLiJEyfOnz//22+/bf7p7OzsLly4cPLkyYMHD27fvt3CwmLSpEmTJk0aNmxYozsTiUhfX/+jjz6q/9i7d2/5F0eOHFm4cCGLxfr++++XLl2qrKz82nOZmZkR0eDBg+W1RTk/P7+NGzc2+cq/n3/+ubi4WCAQyPeNIaLc3FyZTBYYGGhjY7N69ermLxnax86dO7OyspwdQ/G+xU7E1CSoXHBx0cLgm/FxeAUBQLvBT0kAAAAAAGg/rq6ujx49Onz48Ny5cxt17d+/v6Sk5G3fhsnlcsePHz9+/Pja2tpLly6dPHly//7927Zt09fX9/X1/fLLLy0tLesPtra2PnLkSKMZIiIiZs2aNWTIkOPHj/fs2VPBueS9dXV1DRvlT1traWkpzmlgYODs7JyWllbfUlNTI5VK79+/32gTbegICgsL163bYGL8qYqyOdNZWoVMLH7J5Tbxp/T1I2VimUzMZqu0eqY20st8/e07PkePHv3444+ZzgLQXaC8CAAAAAAA7eebb7757bffvvrqq0GDBr333nv17VFRUSdOnLC1tZ0wYULLZlZSUvL39/f39xeJRJGRkSdPnjx16pSvr2/D8uJrrVmzRktL6/Tp002+ElFVVdXLyysmJiYtLa1+Y+izZ88SUcNnrl8rODg4ODi4YYuLi4tQKGz1rUigVfz0008SCc/cbAnTQd7C7XvDtDWH9rH6oWGjWCzIevZNYfEZqVTE4WjoaI+0stzE48p3RZfeezBGJpM0PF5ZpYe97WH51+XlsVnPN1VVP5bJJCrKPcxMF5gYzybq6NVwdXV7A/0pGzduRnkRoN2gvAgAAAAAAO3H1NR027ZtgYGBQ4YMWbBgwYABA1gs1p9//hkSEqKmpvbrr7++6cHkNykqKjpw4MCr7f3797exsTE1NVU8vKysLDExsX///j/++GOjLk9PT19f37179y5atOirr7766quviGjz5s1ubm7Tpk3btGmTubl5TExMSEjI8OHD/f393yo2dGQymWzfvgN6ugFstirTWZqroOikSJRFmn8rc8tkdUmPP66ouGdkOF2T71JRef9FwZHa2nzH984RUU1NflV1irqaLZerUz+E939flwtuJKZ8xOVqGhlOZ7G4xSUXnmZ+UVdX0tP8s/ZcV8uYGM25/2h8QkJCW+xTDwCvQnkRAAAAAADa1Zw5c4yMjJYuXVpf0WOxWKNGjfr3v/+t+Nnk13rx4oWCXWKdnJwUD4+Li5PJZHfv3r17926jLhaL5evrK5PJJBKJTCaTNw4cOPDChQuBgYHjx4+Xt/j7+x88ePBtY0NH9vDhw7y8bGeHFt5I255qavOzs7dWVN2vqkp+tbeg8GRFxV2LXl+ZmS4gIiPDGUT0ouBIZeUDDQ0noSiLiGz67FJX7/fq2Oyc7UQyZ4eLKiq9iKh3z9UJd1xz80LMeyxnsRq/1bSj0dBw0tAwDw8PR3kRoH2gvAgAAAAAAK1jwYIFCxYsaM6RPj4+qampz549S0lJ0dLScnBw4PP5rz2yT58+9aW913JwcFB8gGLyAqKCA15dlI+PT3Z2dmJiYlFRkYODw2sfqW4yNhHduXOnBYGhHfz1119KSnwNDUW7kHcQEkmlUJTB5WhqaDhXVt5v1FtUfIbH0zc1+d97Ts3NlmryB/J4ekQkEmUSsVRVX//2gJraPGUlE3ltkYg4HA2+hrPg5U2prIbDUmub1bQmdbWhcXF/MZ0CoLtAeREAAAAAABjAYrF69+5dv3dz58Lj8fr37890CmgrKSkpGuo2RJ1g32E1VWsH+zAiEomybt8b1qhXKMrQ0R7JYvFEomfVwidKSsbqav0MDT74v95MZWUziaSqXHCjrq5IVdWar9G//s5EPd1xuXkhZWUxOjpeRCQUPhW8/FNby53D7gS1RSJSU7NNStrHdAqA7gLlRQAAAAAAAID/KSkpYbP1mE7xriSSqtraQiWeQfLj2aVlV+SNqqp9bKy28fkDiEgkypJIKm7ddZNKhfJeDXVHG+udaqrWRGRqPLdccCPp8SxNviubrVwu+FNZyahXz8+ZWs7b4nF1y8pKmE4B0F109C2fAAAAAAAAANpTbW0tkRLTKd6VSJRFRHkv9otqsq0svnF2vGRpsaGmJif5SWBdXTERCUVZEklVT/MVLv1vOL53ztjo46rqpJTHgRJpNRFxuFoqyj2IZBWV9ysq7hJJWSyuRFLF7KKaj81WrqurYToFQHeBuxcBAAAAAAAAupo6cTkRSaW1djZ7VVX7EJGGukNdbVF27s6i4nOmJkE2fbaxWUpqarZEpKpiocl35XL4OXk/l5REGhp88ChxUlX1YyvLbw30JrLZymXlMWlPVyWlzBzgfFVF2ZzhtQFAB4O7FwEAAAAAAAC6GmUlYyLi8wfIa4tyurpjiKhamEZEGuqO8tpiPR2dUURUXf2kWphWVf1YS3OIidEsLleLzVbR0x1vZDBNKhWWlF5s12UAQGeA8iIAAAAAAHQ1MpmsrKysxcOlUmkrhgFghLKyGRHJpOKGjVKpiIi4XM2a2rzikgs1NbkNe0WiZ0TE4+lXV6cQkZbmkIa92toeRCQWl7dxcADofFBeBAAAAACAziE6Otr+DWbNmiU/pqysbP78+erq6rq6upqamgEBAcXFxa9OZW1tPX/+/EaNqampy5Yt6927t56enq+vb3R0dJsvCaDNsNkq2lrDKqseCkWZ9Y0lpZeISJPvKq4rf5z6SXbuzoZDikrOE5Gmppuqqg0RFZdeaNhbXBJOROpqdu0QHgA6F7x7EQAAAAAAOgcWi8Xj8Ro1ikSiJ0+eWFtbE1Ftbe348ePj4+Pnzp07ZMiQhISEvXv35uTkxMXFNRwSGhqanp7u6enZsFEoFPr7++fm5n744Yd6enphYWF+fn6XLl3y8PBo42UBtJXePdfcf+T7OHVB757/VFYyLRfEvSg4rMkfpKszhkjG57u8KDjK5ero6/rISFZUFFZeHquvN4Gv4Uwk1dYeUV4em5TykYH+ZBUV85KSi0XFZ9XU+urpjmN6WQDQ4aC8CAAAAAAAnYOXl9f9+/cbNQYHB798+fKXX34hol9//fXmzZtbtmxZuXIlEQUFBbFYrJCQkNu3b7u6uubk5Kxfv/7WrVsPHjx4dfK1a9c+efIkMjLSx8eHiJYtW+bk5DRnzpyMjIy2XxlAm9DQcLa3O5SaviIpZaa8RVdnjE2fbURExOrX90Da089ycnfl5O6S95oYzbLo/S8iImLbWv/0NPOLouKzZeV/yHu1NAdbW21lsRqX+AEAUF4EAAAAAIDO6tKlSz/99FNUVJSxsTERHTlyxNDQMDg4uP6ANWvWDBs2zMDAgIgqKipSU1O1tLQGDhx469atRlOFhoY6OjrKa4tEZGRkNHbs2EOHDsXHx7u5ubXXggBaSEWl9/Ahua+262h7DXK5XVX9WFxXoqZmp6RkWN/F4+n3sw2tqckRCp9yuFpqqn04HI36Xi5Xu6/17t691lZXP5FKRWqqfVRVrYhY7bEYAOhsUF4EAAAAAIBOqaSkZO7cuQEBAV5eXvKWtLQ0Hx8fJSWljIyMxMREMzMzJyenmTP/e9+WnZ1dbGwsEaWnp8sfpq5XXFxcVlYWGBjYsNHGxoaIbt++jfIidGosFldD/b039Sor91BW7vHGXiUTZSWTtskFAF0HtnYBAAAAAIBOafHixeXl5Zs3b5Z/rKyszM/PNzIy8vPzs7Kymjhxoqurq4ODw82bN5uc6smTJ0RkYvK3Mkrfvn2JqLCwsA2yAwAAdB0oLwIAAAAAQOeTlJR08uTJFStW9OzZU96Snp5ORDt27MjMzNy1a9edO3d27tz57NmziRMnNlkilI/V1dVt2NirVy8iKi8vb5MFAAAAdBV4OBoAAAAAADqf77//XklJacWKFfUtpaWlRFRTU3P69GlbW1siGjBgQEFBwcaNG0+cOLF06VIFsykrK9fPUK+qqoqIdHR02iI/AABAl4G7FwEAAAAAoJN5/vz5sWPHJk2a1PB+QzMzMyIaPHiwvLYo5+fnR0QpKSmKJ5TvDNNok2h5tVG+LQwAAAC8Ce5eBAAAAACATmbv3r1isTgoKKhho/wp6bq6uoaNQqGQiLS0tBRPaGNjw2KxGpUXHzx4QETY1wVe60XBkTpxKRGpqfbR0x3/907pO9zKw8xYiaSKw1FvwUCZTMxicdpsR+m/rai8PLai6gERsdkqZiaftM0ZAaAlUF4EAAAAAIBO5vLly7q6uqNGjWrYqKqq6uXlFRMTk5aWVr8x9NmzZ4lo6NChiic0NTX18PC4du3a06dPraysiKiuru7YsWNmZmYuLi5tswjo3PLy/y2qyVZSMtLV9pKXF4WijPwXoSWlv0skLzX5A01NPtHWGt7M2ZgaW1n1KOv5t5WV98ViAY9noKc71qLXFxwOn4iIpPcejJHJJA2PV1bpYW97WP51WVnMs+zvqoWpHA5fS3OYifFsLc3B/3dgE2NbtqKKynsFRafq6opZLC7KiwAdCh6OBgAAAACAzqSsrOzOnTvu7u5sduO/zmzevJnFYk2bNu3ixYuJiYk7d+4MCQkZPny4v79/k9OuWbOmrq5u2rRpZ86cuXr1qp+fX0ZGxr59+1isNrotCzo9Lc3Brv3jLC02EJFUKkp+PKeg8ISOtqex0WyhMDP58WzBy6Z3LWdwbGXlg8SkqZWVDw303zfv8Q8uh/+i4Mij5OlEUiKqqcmvqk5hsdg8nt7//uH+91WkRcVnkx7PEotfmpku1NXxLiu7kvx4jlD4VN6reGyLV2Te4x+u/eP0dMc1Zx4AaE+4exEAAAAAADqTq1evSqXSIUOGvNo1cODACxcuBAYGjh//34dV/f39Dx482Jxpx4wZc/jw4Xnz5k2ZMoWItLW1t27d6uPj04rJoQt79nyzUPjU3u6wjrYXEZmZBN19MDotfbnrgL867Ni8FwclUpGzwwV1dXsi6mW+KjE5oFxwo7gkUl/PVyjKIiKbPrvU1fs1GiiT1WU+28DhqDk7/s7lahJR755rEu64PE5b2N/xMhEpGNumKwIApqC8CAAAAAAAncnkyZNlMtmben18fLKzsxMTE4uKihwcHExMTF49pk+fPq+dYfr06R988MHt27elUqmbmxuHw2nN3NClFRSdVFezk1fEiIjHM9DRHlFYdLqi8h5fo3/HHFtRcVtD3V5eW5QzMgwoF9yoqLynr+crEmUSsVRVLV8dWF2dWlv7Ql/PT15bJCIeT19He0RpWbRYUsHl8BWMbdK7rAgAmILyIgAAAAAAdCk8Hq9//xaWIbhc7uDBg5s+DqCBOnGpWCwwMgho2KiqakVElZUPFBfFmBork4m1tT35Gs4NG2tq8oiIx9UmIqEoU1nZTCKpKhfcqKsrUlW15mv0Z7E4RFRbW0BEjebX0OhfWhZdXf1Ek++qYKxi77IiAGAQyosAAAAAAAAALSd/56CSklHDRlUV+R5BxR1zLIvFtbL4pmFLXV1x/otQFourozOaiESiLImk4tZdN6lUKD9AQ93Rxnqnmqq1ikovIip/ecPMdEH98OrqVCKSlxcVjG27FQEAg7C1CwAAAAAAAEDLiURZRMTlajdsVFE2IyKx5GXHHNtIadmVuw9G1dS+sOj1lbqaLREJRVkSSVVP8xUu/W84vnfO2OjjquqklMeBEmm1qqqFhoaTQHDjReExiaRSLKnIfxFaXBJBRPJtYRSMbbcVAUB7wt2LAAAAAAAAAC3HYikRkVhc3rBRIhUSEZej1THH1hOJnmVk/au0LEpFpXdf693aWu7ydps+29gsJTU1WyJSVbHQ5LtyOfycvJ9LSiINDT6wttqa/Hh2+tNVGZlfEUllMqmx0YcvCo6oqdo0ObatVwQA7Q/lRQAAAAAAAICWU1IyJCKR6FnDRrG4jIh4PL2OOVausCjsaeZqIlbvXl+YGgex2Ur1XRrqjo0O1tEZlZP3c3X1EyJSV7Md4BRdXBJeXZ2qpGSkreUhePknEamp9W1ybJuuCAAYgfIiAAAAAEATNm7cSER2dnaTJ0+ubxSLxSwWq2WbC4vFYg6Hw2KxWtD7KqlUyma38K1HlZWVGhoaCg6oqKiora3V03u7v9i32xLa7iq8rbZbQpPXqH1iKB5bV1enqqraKjM3WsLly5dv3bpFRKqqqitWrGhBvHagqmJJxBLVPG/YWFWVTK/sf9JxxhJRadmV1PRlmnyXvtY/KSubNeyqqc2rqLjH13Bu2C6v+vF4+jJZnUj0nMfTNTKcUd+bk7dbScmQy9VWPLZNVwQATMG7FwEAAAAAmrBp06bQ0NA//vhD/vHo0aNDhw7l8/kqKiq2tra7d++WSqWvjrK2tp4/f36jxsjIyAEDBmhoaBgZGU2fPv3atWvN720kNTV12bJlvXv31tPT8/X1jY6Obv6K7t69O3bsWF1dXT6fb2xsvGDBgpcvX/Nes5KSEltbW3f3/z4sGR0dbf8Gs2bNaucltMVVaM4C220JzbxGbR2j3mu/k5cvXx4wYICampq6urqFhUWjsc2cWfES4uPjQ0NDv/vuO3mVv2NSUjLS0hwseHmz/rY7mUxcWPybkpKxhkbj+/g6yFgievb8Wy6Xb2uzr1FtkYjEdeWPUz/Jzt3ZsLGo5DwRaWq6SSTCO/c9nmZ+Ud9VU5tfXHJBV2dMk2PbdEUAwBSUFwEAAAAAmuDh4ZGWlrZz504iOnTo0MyZM8vKypYtW7Zo0aLKysrg4OBNmzY1GhIaGpqent6o8fjx476+vuXl5atWrfL19Y2IiPDz83vy5ElzehsRCoX+/v4HDhwYO3bswoUL09LS/Pz8FNfy6t2+fdvLy+vOnTsffvjhl19+qaWltXfvXm9v71dLP0FBQXl5efUfWSwW7xUSiSQ5OVle+Wq3JbTRVWhyge22hOZfozaNUe+138no6Ohx48ZlZWUFBgYuWrRIKBQGBwevX7/+rWZucglffvllWlra+++/35zlMMjcLFgmEz9OXVBSGikQ/Jn8eLZI9Nza6gciFhHl5u+Lu9nzec62jjNWLBZUVT9RUe6Vm/9L5rOvG/5TWnZFXd2Oz3d5UXA06/nmysoHFZX3MzK/LC+P1debwNdw5nI1tbWGFZdEFBSeEIsFlZX3kx/PVlYytej1JREpHvuOKwKAjoklk8mYzgAAAAAAwAx7e/upU6euW7dO8WE+Pj4XL16Uf+3k5CQSiW7duqWpqUlEeXl5FhYWurq6+fn5RJSTk7N+/fpbt249ePCAiObNm7dv3z75wNraWktLS4FAkJ2dra1Un555AAAgAElEQVStTUSFhYU9evSwt7e/d++e4t5XI61YsWLbtm2RkZE+Pj5EVFBQ4OTkpKamlpGR0eSq58yZc/z48fj4eGdnZ3mLt7d3dHT0yZMnp06dWn/Yzz//vHz5cnV1dSMjo+Tk5DfNFhwcHBYWdvfuXV1d3XZbQhtdBcULNDY2brclNPMaMfidJCJPT8/Y2Nj09HQrKysiqqio6NGjh0QiEQgEHA5H8cxvu4TZs2dHRESUlJQoXs66detOnTqVlJTU5MIVmzZt2tUYoa1NiIJj7t4fqaxsZm93pL6lqPhc+tPP5Jsjc7maPXusNDWZJ+/KzQvJfPZ1T/OVPXu8/vnu9h9bWnYl+fHs105oZvqpRa8v6+qK055+VloWVd9uYjTLove/2GwVIqqpyXmctqii4o68S0PdwcpiE58/QP5R8dh3WRERpaYvKy27MnhgE1e5uCT8ceqnqHgAtA+8exEAAAAAoLkEAkFiYmJwcLC8YkJEpqamXl5eMTExdXV1PB6voqIiNTVVS0tr4MCB8hfG1UtOTs7NzZ02bZq8qkVEhoaGY8aMuXDhgkAgyMzMVNCrpdV4y9TQ0FBHR0d5LYaIjIyMxo4de+jQofj4eDe3Jh4//PPPP52dnevrVkQUGBgYHR2dkJBQX7pKSkpauXLld999t2/fPgUPtF66dOmnn36KiooyNja+f/9++yyh7a7CqyEbLvDVJG23hOZco3aIoeA7SUTZ2dk9evSQ1xaJiM/nDxo06Nq1ayKRSCwWK565tZbQoRjoT9TXm1BZ+VBGUr5Gfxbrf6+bNDNdIJXVqCj37DhjdXW8hw/JVbAcHk+/n21oTU2OUPiUw9VSU+3D4fzvHaDKyj2c3jtXVf1YJHqmoe7Q6PFqxWPfZUUA0DHh4WgAAAAAgObicrnXrl37/PPP61sEAsHDhw/HjBkjr5jY2dnFxsbGxsYeO3as0Vj5U8aDBg1q2Cj/mJSUpLi30VTFxcVlZWXe3t4NG21sbIjo9u3bipdQV1c3duzYJUuWNGzMzs4mIl1dXflHkUg0Y8YMd3f3pUuXKpiqpKRk7ty5AQEBXl5eTS6wFZfQdldB8QLbbQnNuUbtEIMUfieJ6P3338/JyYmMjJR/fPLkydWrV0eOHKmurt7kzK21hI6GxeLy+QM0+a6NKmIiUVZB4QlNzUFvGsjgWMWUlXtoa4/gazg3rA/Wn1ZdzU5Pd9yrr25UPPZdVgQAHRPKiwAAAAAAzaWurj5s2DATExMi2r59+/LlywcOHCiRSFavXt3kWPlNXjExMQ0b5Q8dJyUlKe5tNJX8RYHyGPX69u1LRIWFhYpj8Hi8Xbt2zZw5s76lsLBwz549PB7P19dX3rJq1aq8vLzQ0FDFuyovXry4vLx88+bNzVlgKy6h7a6C4gW22xKac43aIUaTgoODnZ2dfX19hw8f7u3t3a9fPxMTE/kGLG8187ssgVlVVUmPUxfk5u9r8kihKKufbaiykmkLzsLU2LbT4lQFhf95nLpA8PJmW6QCgHeBh6MBAAAAAFpi7dq11dXVRGRvb6+qqtrk8dbW1q6urtHR0fv37w8ICJBKpUeOHDl16hQRSSQSxb2NppJvstHoRrZevXoRUXl5+VutIiIiIigoqKioaPv27Q4ODvKW3bt3nzlzplG5p5GkpKSTJ0+uWbOmZ8+ezVlgGy2hda+C4gUytYRXrxEjMV6lra3dq1ev+/fv37p1i8fjSaVSLpdbUVHxtjO34p/n9qStPaK2No9IRtT02/10tD1bfCKmxradd0glI5LxNZw4bPVWzAMA7w53LwIAAAAAtERVVVVqauqBAwdKSkrc3NxevHih+Hg2m33gwAFjY+P58+cbGxsbGxuvWLFi/vz5RGRvb6+4t9FUysrKRFRaWtooDxHp6Og0M//Tp0/9/f39/Pw0NTUvX74sfw46Pz8/MDBw3rx5TW7U+/333yspKa1Y8b+dGdp/CdTaV0HxAtt/Ca+9Ru0f403c3d3PnTv3008/vXjxoqioKCws7OXLlxMmTMjKynqrmVtrCe3Msvc6W5u9tjZ7zUw+YTpLd2FkOF3+Pbfu8/pdpwGAKbh7EQAAAACguWQymUwmY7P/+0t6a2tra2trNps9Z86cyMjIuXPnKh7u4ODw6NGjkydPJicnm5iYjB49+o8//qD/K2wp7m1Ivs1Io32B5dUZAwOD5izkyJEjCxcuZLFY33///dKlS+X1HSL6+eefi4uLBQJBYGCgvCU3N1cmkwUGBtrY2NQ/2fr8+fNjx45NmTKl0R1n7bOENr0KihfYnkt40zVq5xhvkpKS8ujRI09Pz4ULF8pbJk+eHBcXt3Xr1jNnzixfvrz5M7/7n2cAAGAW7l4EAAAAAGiuzZs3czic+r0s5PT19en/dt5QoLa29smTJ7W1tUFBQT/++ONnn33m5OR08+ZNExMTXV1dxb2NprKxsWGxWI3KMQ8ePCCi5myzGxERMWvWLEdHx8TExFWrVjWsWxkYGDg7O6elpd3/PzU1NSKR6P79+/InWOX27t0rFouDgoKav8BWXELbXQXFC2zPJSi4Ru0ZQ4GHDx8S0YgRIxo2jh49mojKysreauZ3/PMMLSCTiWWyxq8sAABoMZQXAQAAAACaS/7mu6ioqIaN+/btIyInJyfFY6urq21tbYODg+tbcnJywsLC/P39m+xtxNTU1MPD49q1a0+fPpW31NXVHTt2zMzMzMXFpclVrFmzRktL6/Tp06++VTA4OPje39nZ2VlaWt67d+/f//53/WGXL1/W1dUdNWpU8xfYiktou6ugeIHtuQQF16g9YyjQr18/Ijp9+nTDxpMnT8qnfauZ3/HPM7yVouIzDxL9/0qw+TPe8s59j/wXB4mkTIcCgE4P5UUAAAAAgOYaP368g4PDrl271q9ff/PmzbCwsOnTp4eHhw8cOPDVLX0b0dbW9vLyOn369IEDB8rKym7duuXn59ejR48ffvihyV4i2rt3L5fL/frrr+Uf16xZU1dXN23atDNnzly9etXPzy8jI2Pfvn3yvZ63bdvW8OCGysrKEhMTLS0t5ffuNRQREdGcb0JZWdmdO3fc3d3rH31tzgJbcQltdxUUL7DdltDkNeoI30l7e/sxY8YkJSWNGzfuyJEjN27cWLly5fHjx+3t7SdNmtTkzG+1BGgthUWnn6QtFYsFpibzTIxnSyRVTzO/yM7ZxXQuAOj08O5FAAAAAIDmYrPZZ8+e/fjjj9etW7du3Tp54+TJk3fu3MnlNv2/1gcOHJgxY0ZQUJD8qdsBAwYcO3aMz+c3p1cmk0kkEpnsv3vUjhkz5vDhw/PmzZsyZQoRaWtrb9261cfHR94rlUobHtxQXFycTCa7e/fu3bt3G3WxWKwmi0pEdPXqValUOmTIkLddYGstoU2vguIFts8SmrxGHeE7yWazjx8/HhwcfPz48d9//13e6OHhceDAASUlJSJSPPNbLQFaS27eL6oqFs4OERwOn4h6mC6+ddct/0WoeY9lTEcDgM6N9dr/zAAAAAAAdAf29vZTp06tL3+8iY+Pz8WLF+s/SqXSzMzMx48fq6qq9u3b18zMrPlnlMlkjx49ysjIGDBgwKvPvSrufZVYLL59+7ZUKnVzc+NwOA27Nm7caGlpOWPGjOZnaxXttoS2uwpvi5EldJwYOTk5SUlJQqHQ1ta2b9++De83fNuZFSxh9uzZERERJSUlimdYt27dqVOnkpKS3moJr5o2bdrVGKGtTcg7ztOhiCUVNxP6mZrMtey9vr4xKeXjcsGNoW7pLFZXu/eouCT8ceqnqHgAtI+u9hMEAAAAAKCtsdlsKysrKyurFoxlsViOjo6Ojo4t6H0Vl8sdPHjwq+3p6ekHDhyQb4jcztptCW13Fd4WI0voODF69OjRo0ePVpn5TUtofywWi6irlaVYLI7je2dUlHvVt4glFVXVyTraI7pebZGIiGR4uB6g3XTJHyIAAAAAAK3p/v3706ZNGzJkyPLly5nO0ixPnz4NDw83NzdnOkjLYQldLEaLHTx48OLFiwkJCe15Ug0NDaLi9jxjO+Cw1TT5A+Vf5+XvE9XklpVdkcmkPcyCFQ/spMSSSjU1ftPHAUBrQHkRAAAAAKAJw4YNk0qlneghu7FjxzId4V1hCa2lg8RoMZlMJpVKXV1dG74fs60ZGxuLxX+22+naX9bz76RSIRGpqfVls1WYjtMmamtfGBoaMZ0CoLtAeREAAAAAoAmnT59mOgJANzV37ty5c+e280kdHR1fVvwglQrZbNV2PnX7GOqWLhRlvnyZ8Oz55gePJgx0uaXEM2Q6VCurFia6u7fOCxAAoElspgMAAAAAAAAAdCAjRoyQySTlgutMB2ldMiJp/QdVFQsjw4DevdbIZOKyshgGY7UFqbT25cs4b28vpoMAdBcoLwIAAAAAQNcnlUqbPgiAiIiMjY3dBg0pLjnDdJDWlJO7+8Zf5o0qiVyuLhHV1OYxFKqtlJb9LhYL/f39mQ4C0F2gvAgAAAAAAF1WamrqsmXLevfuraen5+vrGx0dzXQi6BwWL/m0pPSSUJTJdJBWo6ZmR0RlgmsNGwsKjxKRulo/ZjK1FdmLgpDx4ye8aU9zAGh1KC8CAAAAAEDXJBQK/f39Dxw4MHbs2IULF6alpfn5+V27dq3pkdDtzZgxw9bW7nn2eqaDtBpdHS91Ndv8Fwee52ytqLhbXHLhcerCktIoDQ1nXR1vptO1poKiUy8rHn7zzddMBwHoRrC1CwAAAAAAdE1r16598uRJZGSkj48PES1btszJyWnOnDkZGRlMR4OOjsPh7N69Y+TIkYb6MTo6XeMVfmy7vgeepAc/z/7xefaP8iY93fFWFhtYrK5TGZBIKnJzNy9YsMDJyYnpLADdSNf5IQIAAAAAANBQaGioo6OjvLZIREZGRmPHjj106FB8fLybmxuz2aDj8/T0DAiYcf7ccnu1CBVlc6bjtAIVlV5O750VibKrhelstoqaqpWSkjHToVqXND0jWE2NNmzArYsA7QoPRwMAAABA98VisZiOAG2luLi4rKzM2/tvT33a2NgQ0e3btxkKBW2udf+lPnBgv62dxZO0mWLxy1acllFsFZVeujqjtLWGdbnaImU+2yB4ef18+G96enpMZwHoXlBeBAAAAIDuS0NDo6qqiukU0CaePHlCRCYmJg0b+/btS0SFhYXMZII2VlFRwefzW3FCNTW1c+fCVFQqn6TNFovLWnFmaG2y59k/5uXvCw09MGTIEKbDAHQ7KC8CAAAAQPdlbGycnZ3NdApoE+np6USkq6vbsLFXr15EVF5ezkwmaGM5OTnGxq18R565ufnVq1HqGgVJKX5CYXrrTg6tQiqtSXu6JO/FrpCQkBkzZjAdB6A7QnkRAAAAALovR0fHu3fvMp0C2oSysjIRlZaWNmyU36yqo6PDTCZoY/fu3XNwcGj1ae3t7e/cSbDrZ5yY4p//4qBMJm71U0CLvaxISErxF4piL126OH/+fKbjAHRTKC8CAAAAQPc1cuTItLS0p0+fMh0EWp/8LrZGm0TLq40GBgbMZIK29PTp07S0NC+vNtnl2dDQMDY2Zvnyhc9zNjxKHltWFkMkbYsTQfMJRZlpT5c8SprsOtD0zp2EUaNGMZ0IoPtCeREAAAAAuq/hw4cbGhoePnyY6SDQ+mxsbFgsVqPy4oMHD4gI20Z3SYcPHzY0NBw2bFgbza+iovLtt98mJyd6eNgkPZ55/5F71rONZeVXa2rzpdKaNjop/J20TlxaUXEnN39f8uOpd++7q6o9PHv27JUrv/fp04fpbADdGksmkzGdAQAAAACAMatXrw4NDU1NTW3dHSGgI/D09ExISHj06JGVlRUR1dXV9evXTygUZmdnY9PwLqaiosLGxmbOnDnffvttO5wuKSnp4MGDv/12PiMjrR1OB41oaemOGzfm448/8vHx4XA4TMcBAJQXAQAAAKB7Ky0t7du3b1BQ0ObNm5nOAq3s8uXLEyZMcHR0XLt2rY6OznfffRcVFRUREeHj48N0NGhln3/++b59+1JTU/X19dvzvKWlpcnJyWVlZSKRqD3P2z2x2WxtbW0LCwsLCwv8hgCgQ0F5EQAAAAC6uz179qxYsSIxMdHa2prpLNDKTpw4MW/ePPmOLtra2uvWrVu2bBnToaCVpaWlvffee1u3bl28eDHTWQAAuiOUFwEAAACguxOLxW5ubmKx+MaNG3hEuusRi8W3b9+WSqVubm54jrLrqaioGD58OJfLjY+P53K5TMcBAOiOUF4EAAAAAKDc3Fw3NzcHB4eIiAhUoAA6C6lU+v777yckJMTHx/fs2ZPpOAAA3RR2jgYAAAAAIDMzs7CwsNjY2NmzZ9fUYBNYgE6gpqZm1qxZUVFRZ8+eRW0RAIBBKC8CAAAAABARubm5nTt3LiIiwtvbu6ioiOk4AKBIUVGRt7d3RETEuXPn3NzcmI4DANCtobwIAAAAAPBfo0eP/vPPP/Py8lxdXcPCwpiOAwCvFxYW5urqmpeX9+eff44ePZrpOAAA3R3KiwAAAAAA/9OvX7/4+PiRI0dOnTp11KhRCQkJTCcCgP9JSEgYNWrU1KlTR44cGR8f369fP6YTAQAAtnYBAAAAAHid+Pj4pUuXJiQk9O/ff/LkycOHD7ezs9PV1eXxeExHA+hG6urqSktLk5OT4+Lizpw5c+/evUGDBu3cuRMPRAMAdBwoLwIAAAAAvNGNGzcOHToUGRmZm5vLdBaAbs3MzGz8+PGzZs0aPnw401kAAOBvUF4EAAAAAGhaXl5eampqeXl5XV0d01k6n+fPn69fv37RokUuLi5MZ2GARCJZtWqVk5PT7Nmzmc7S+XC5XB0dHRsbG1NTU6azAADA66G8CAAAAAAAbSg9Pd3Dw8PW1vbChQuqqqpMx2FGWFhYQEDA2rVr169fz3QWAACAVsZZt24d0xkAAAAAAKBrys3NHTlypKmp6cWLF9XV1ZmOw5h+/fr16tVrxYoVKioqeLYXAAC6GC7TAQAAAAAAoGsqKioaPXq0hoZGZGQkn89nOg7DZs+e/fLly2XLlmlqai5cuJDpOAAAAK0G5UUAAAAAAGh9AoHAx8entrb2+vXrenp6TMfpEIKDgwUCweLFi5WUlIKCgpiOAwAA0DpQXgQAAAAAgFZWXV3t5+f34sWL69evm5iYMB2nA/niiy+qqqoWLFigoaEREBDAdBwAAIBWgPIiAAAAAAC0ptra2g8++CAlJSU2NtbCwoLpOB3Ot99+W1tbO3PmTA0NjQkTJjAdBwAA4F1h52gAAAAAAGg1EolkxowZv//+e0xMjIuLC9NxOiiZTPbJJ58cPXo0MjLS09OT6TgAAADvhM10AAAAAAAA6CJkMtmCBQsuXLgQHh6O2qICLBbrl19+mThxoq+v740bN5iOAwAA8E5w9yIAAAAAALSOlStX7t69++zZsz4+Pkxn6QTq6uomT558/fr1mJiYAQMGMB0HAACghVBeBAAAAACAVrB27drvvvvu2LFj06ZNYzpLpyEUCidMmJCYmBgbG2tnZ8d0HAAAgJZAeREAAAAAAN7Vjh07li9fvm/fvqCgIKazdDLV1dVjx47NzMy8fv06dsIBAIDOCOVFAAAAAAB4JwcPHgwKCtqyZcuKFSuYztIpCQSCUaNGlZeXX7t2zdTUlOk4AAAAbwflRQAAAAAAaLmwsLCAgID169evXbuW6SydWFFRkaenp1gsvnbtmpGREdNxAAAA3gLKiwAAAAAA0ELh4eFTpkxZtGjR9u3bmc7S6eXm5np4eGhoaFy9elVXV5fpOAAAAM2F8iIAAAAAALTE1atXx48fP3369AMHDrBYLKbjdAXPnz/38PAwMTGJiorS0NBgOg4AAECzoLwIAAAAAABvLSEhwdvbe8yYMf/5z384HA7TcbqOtLQ0Dw+Pfv36XbhwQUVFhek4AAAATUN5EQAAAAAA3s6jR488PT1dXV3Dw8OVlJSYjtPVPHz4cOTIka6urufPn1dWVmY6DgAAQBNQXgQAAAAAgLeQnp7u4eFha2sbGRmJ2+vaSHx8/OjRo0ePHv2f//yHy+UyHQcAAEARlBcBAAAAAKC5cnJy3N3dDQ0Nr1y5wufzmY7TlcXFxY0dO3bKlCkHDx5ks9lMxwEAAHgjlBcBAAAAAKBZioqKRowYweFw/vjjDz09PabjdH2XL1/29/efN2/e7t27mc4CAADwRrjNHgAAAAAAmiYQCMaNG1dbW3v9+nXUFtvHmDFjjh8/Pm3aNCUlpa1btzIdBwAA4PU469atYzoDAAAAAAB0aNXV1RMmTMjJybl69aq5uTnTcboROzs7CwuL1atX83g8d3d3puMAAAC8Bu5eBAAAAAAARWpraz/44IOUlJTY2FgLCwum43Q7M2fOrKioWLx4sbKy8sqVK5mOAwAA0BjKiwAAAAAA8EYSieTjjz+Oi4uLiYnp168f03G6qUWLFtXW1q5YsYLP53/yySdMxwEAAPgblBcBAAAAAOD1ZDLZggULLly4cPHiRRcXF6bjdGv/+Mc/SkpKFi5cyOfzZ8yYwXQcAACA/0F5EQAAAAAAXu+zzz47fPjw2bNnPTw8mM4CtGHDBpFINHv2bA0NDT8/P6bjAAAA/BfKiwAAAAAA8Bpr167dsWPHsWPHfHx8mM4C//X999+/fPnygw8+OHfu3Lhx45iOAwAAQETEkslkTGcAAAAAAICOZceOHcuXL9+3b19QUBDTWeBvpFLpzJkzz549e+nSJewlDQAAHQHKiwAAAAAA8DcHDx4MCgrasmXLihUrmM4CryGRSKZPnx4VFXXlyhVXV1em4wAAQHeH8iIAAAAAAPxPWFhYQEDA+vXr165dy3QWeKPa2tpJkybdunXrjz/+sLe3ZzoOAAB0aygvAgAAAADAf4WHh0+ZMmXRokXbt29nOgs0obq62sfH58mTJ7GxsX379m3YlZmZaW5uzuXiVfsAANAe2EwHAAAAAACADuHq1avTpk376KOPtm3bxnQWaJqamlp4eLi5ufno0aOfPXtW356UlDRkyJAzZ84wmA0AALoV3L0IAAAAAACUkJDg7e09ZsyY//znPxwOh+k40FzFxcWenp61tbWxsbEmJiZ37tzx9vYWCATOzs53795lOh0AAHQLKC8CAAAAAHR3jx498vT0dHV1DQ8PV1JSYjoOvJ3CwsIRI0bweLxvv/02ICBAJBJJJBIiunbtGraWBgCAdoDyIgAAAABAt5aenu7h4WFraxsZGamiosJ0HGiJZ8+eDRo0qLi4mMViyWuLXC533Lhx4eHhTEcDAICuD+9eBAAAAADovnJyckaPHm1ubn7u3DnUFjuvR48elZWVEZG8tkhEYrH4woULKSkpjOYCAIBuAeVFAAAAAIBuqqioaMyYMRoaGpGRkXw+n+k40EInTpyYNGmSRCKRSqUN27lc7o4dO5hKBQAA3QcejgYAAAAA6I4EAoGXl5dAILh+/bqJiQnTcaCF9u/f/8knnxDRa/9mp6SklJubq6+v3+65AACgG8HdiwAAAAAA3U51dbWfn19BQUFUVBRqi52XUCiMiYkhIi6X+9oDpFLpTz/91L6hAACg28HdiwAAAAAA3Uttbe2kSZNu374dGxtrZ2fHdBx4V8nJyV999dWZM2e4XG5dXV2jXh0dndzcXFVVVUayAQBAd4C7FwEAAAAAuqxGL+MjIolE8vHHH8fFxV28eBG1xa6hX79+p0+ffvDgga+vL4vF4vF4DXtfvnx55MgRprIBAEB3gPIiAAAAAEDXlJWV5e3t/fLly/oWmUy2YMGCCxcuhIeHu7i4MJgNWp2Dg8OZM2f++uuvUaNGUYPHpaVS6ebNm18tNAMAALQWlBcBAAAAALqmHTt2XL161cPDo6SkRN6ycuXKw4cPnz592sPDg9ls0Ebc3NwuXrwYFxc3bNgwIuJyuTKZLCMjIzIykuloAADQZeHdiwAAAAAAXZBAIDAxMREKhTwez8LC4urVq7t37/7++++PHTs2bdo0ptNBe4iKilq9evWdO3eIaNiwYTdu3GA6EQAAdE0oLwIAAAAAdEE//PDDmjVrxGIxEfF4PDU1tZcvXx48eHD27NlMR4N2FRERsXr16sTExNu3b+OJeAAAaAsoLwIAAAAAdDVisbhnz575+fn1LRwOR0VF5a+//nJwcGAwGDDlypUr9+7dW7VqFdNBAACgC0J5EQAAAACgqzlx4sSHH37Y6H/1uVyuqqpqVFSUm5sbU8FaRUpKyvnz569fv/7o0aOSkpKqqiqmEwF0Ndra2ubm5k5OTqNHj/bz89PR0WE6EQB0aCgvAgAAAAB0NS4uLg8ePJBIJI3aORyOsrLyxYsXO+nWLtHR0evXr79+/bqhoaGXl1f//s6Ghobq6upM5wLoasrLyzMzs27dunX9+nU2mz1jxoz169f36NGD6VwA0EGhvAgAAAAA0KXExcUNHz5cwQGmpqYPHjzQ19dvt0jvLjMz87PPPjtz5syECRP+8Y9lXl5ebDab6VAAXV9FRcWxY8e2bPkxPz//n//852effaaiosJ0KADocPCfZAAAAACALmXLli08Hu/Vdi6Xq6ys/PnnnyclJXWu2uKpU6fee++9lJSU33+/FBER7u3tjdoiQPvg8/kLFixITHz0xRdrv/vuO1dX18zMTKZDAUCHg7sXAQAAAAC6jszMzD59+kil0oaNPB5PJpPNnTt3/fr1xsbGTGVrAZlMtmnTpi+//DI4OHjLlh9eWzYFgPaRnZ09adL72dnZv/3227Bhw5iOAwAdCH7pBwAAAADQdWzfvp3D4dR/5PF4HA5nzpw5WVlZISEhnau2SEQrV65ct27dnj27d+zYjtoiALPMzc2vXYsdNmzYqFGj/vjjD6bjAEAHgrsXAQAAAAC6iJcvX5qYmFRXVxMRj8cTi3KE9z4AACAASURBVMWTJ0/evHlznz59mI7WEr/88suiRYuOHTs6ffp0prMAwH9JpdIZMz68cuXKzZs3ra2tmY4DAB0C7l4EAAAAAOgi9u7dW11dzeFwWCyWn59fUlLS6dOnO2lt8erVq8HBwevXr0NtEaBDYbPZoaEH+/Tp4+vrKxAImI4DAB0C7l4EAAAAAOgKxGJx7969c3Nzx40bt2nTpv79+zOdqOVqamree+89R0fH06dPsVgspuMAQGMvXrxwdHSaOXPmjz/+yHQWAGAel+kAAAAAAADQCk6fPm1jY3Py5MmhQ4cyneVdbd26NS8vLzr6CmqLLfb48eO4uLiGLRwOp3fv3i4uLnw+v9HBCQkJ169fv3v3XlVVlY2NjafniPHjx786p1gsZrFYDV/u2YqkUmkzNwQXi8XyW3Tf1KsgpEwmKy8v19HRaXlQICIiY2Pjb77ZsHjxksDAwPfee4/pOADAMNy9CAAAAACdQE5Ozvnz56NjYu7cv1dUWFhdUcl0om5Bja9hYGjo4tx/lJeXv79/jx492uGkRUVFlpaW//zn52vXrm2H03VVISEhn3668NV2AwODvXtDJk2aJP8oFotXr16zZcsWItLR0ZFX34ho3Lhxx44drS/DHT16dM+en+7duycWi62srJYsWbxo0aJmVgMVS01N3bPnp3PnzgkEgmHDhi1f/o9Ro0a96eDIyMgvvvgyOTlZU1PTy8tr0aKFHh4e9b2KQ5aVlf2///f50aNHhUIhn8/38fHZs2e3vr7+uy+h25JIJAMHDjIzMwsPD2c6CwAwDOVFAAAAAOjQHj58+MVXX16IuMBVVeEPc1FzsFEyNuDw1ZjO1S1IKqprXxRVP0qtiLsjFoom+E745usNjo6ObXrSzZs3f//99zk52WpquMotJy8vfvnllwEB0+QtRUVFCQkJX331Ly6X+/DhA0tLSyKaPHnKb7/95unp+csvP/ft21csFsfFxe3f/+8jR474+vqeP3+OxWIdOnRozpzAvn37Tpw4USgUhoWF5ebmbtjw9RdffPGOIYVCYf/+A3Jzcz/88EM9Pb2wsLDs7OxLly42LBrWO378+Ecffdy7d++PPvooNzf35MmTHA4nISG+b9++RKQ4ZG1t7YgRnvHx8XPnzh0yZHBCwq29e/cOHTo0Lu7GOy6hmzt//vz7709OTU21srJiOgsAMAnlRQAAAADooEpLS7/48suQkBC+o63Rp9N1x7qzeDymQ3VTsrq60t+vF/xyouLh4wULFnyzYYOurm4bncvJycnd3X337l1tNH83IS8v/vzzT59++mnD9s2bN69evWb79m3Lli2LiYkZNcp75MiRV65ENbwVsa6uztHR6fHjx9euxbq7uzs5OYtEolu3EjQ1NYkoLy/PwsJSV1c3Pz/vHUOuWLFy27ZtkZEXfHx8iKigoMDJyVlNTS0j42mjI2tray0trQQCQXb2c21tbSIqLCzs0cPc3t7+3r27RKQ45L59+z75ZMGWLT+sXLlSPuGnny4MCQm5dSvB1dX1HVfRnUkkEnPznosWLXr3WjMAdGrYORoAAAAAOqK//vrLpp9daNhJyx9X9wsP0fP1Qm2RQSweT8/Xq194iOWPq0PDTtr0s/vrr7/a4kR5eXkPHz709/dri8mBiGxsbIjo+fNsItqw4Rsi2rRpY6PHnHk83t69IbNmzSovLxcIBImJiT4+PvKyHRGZmpp6eXmVlpbW1dU186QFBQV79uy5detWo/bQ0FBHR0d5bZGIjIyMxo4dm5mZGR8f3+jI5OTk3Nzc8ePHy2uLRGRoaDhmzJj79+8LBIImQx45ctTQ0DA4OLh+wjVrVh869KuBgUEzlwCvxeFwxo8ff+nSJaaDAADDUF4EAAAAgA7n+PHjnl4jWY42DrHHDKb6EPb36CBYLIOpPg6xx8jRxtNr5PHjx1v9DHfu3GGxWIMGDWr1mUHu+PETRGRlZUlE9+7dMzQ0HDx48KuHubu7//prqJ+fH5fLvXYt9vPP/199l0AgePjw4ZgxY3hNVfyLi4tDQkJGjfI2M+uxZElwdnZ2o96ysjJvb++GjTY21kR0+/btRlPl5eUR0aBBAxs2yj8mJSU1GTItLc3Hx0dJSSkjI+P8+fN37twxNTWdOXNmr169FC8BmjRkyOD79+9LpVKmgwAAk7BzNAAAAAB0LPv27VuwYIHJ/ICeXyxmcfDr8A6Hw1e3/ve3z7/Z89FHH1VWVs6fP78VJ8/KyjI0NKy/Qw3eUXZ2zr179+RfZ2VlRUfHnD17VkdHJyAgoKioSCAQNFnJVVdXHzZsmPzr7du3P3v2/MKFCxKJZPXqf75pSFlZ2ZkzZ06ePBUTEyMWi52cnNauXTtxov+AAQMaHvbkyRMiMjExbtgof5FiYWFRoznlr/aLibla/3QzESUnpxBRUlLS0KFDFYSsrKzMz883MjLy8/OPiIiQH2Zra3vw4IHXllbhrVhbW1dVVRUVFRkZGTGdBQAYg/IiAAAAAHQg0dHRixYvNlseaL4yiOks8EYsDrvXv4LZGmoLFy+2tLRUsNXv2xIIBFpaWq01G2zatGnTpk0NW8zMzI4cOaynp/fs2TMiMjQ0bP5sa9d+UV1dTUT29vaqqqqvHlBQUDB3blBUVJRMJvPw8Pjxxy0TJ0580x2C6enpRNToJZ7yg+W7VzdkbW3t6uoaHR29f//+gIAAqVR65MiRU6dOEZFEIlEcUn6iHTt29OnTZ9eunUOHDo2Li/v8839OnDjp0aOHb/UdgFfJfxlQXl6O8iJAd4byIgAAAAB0FOnp6ZOnfqA7wdN8xVyms0DTzFfMrcnInjz1gzsJt/r06dMqc4rFYi4Xf0lpNUFBQWPGjJZ/zeFwLC0t7ezsVFRUiMjc3FxFRUX+0HEzVVVVpqWl3bhxY82atW5ug58/f2Zs/Ld7DwsLCyMjI7lc7pIlSwID5yjeZFxZWZmISktL/36KKiLS0dFpdDCbzT5w4N9+fv7z53+ybNk/pFKpVCqdP39+SEiIvb294pDyU9TU1Jw+fcrW1paIBgwYUFBQuHHjxhMnTixdurT53wF4FYfDISKxWMx0EABgEh42AQAAAICOYuGSxWRmaLF1DV622DmwWJZb15CZ4cIli5mOAq/n6uoy7f9MmTKlf//+8toiEbHZbGtr66dPn752h5a4uDh9fYPVq9fIZLKGr9WztrYODAzcvPnburq6yMjIRqPs7OwuXIj46KOPDh486OTkbGlptWLFymvXrjW6wVBOXprMyMhs2CgvBRoY6L96vIODw6NHD/fv3/fpp59u2PB1QkK8nZ0tEdnb2ysOaWZmRkSDBw+W1xbl/Px8iSgl5bHC7x8AADQLyosAAAAA0CGcO3cu+nKU+fplbGUlprO0AplYIhXVtHx8J9knga2s1HPjiujLUefPn2c6C7w1V1dXgUBw+PDhV7v27/93SUnJwIGumzdv5nC4jSqJ+vr6RNRoqxYi4nK548ePDw09WFhYcO7c2aFDh+7fv3/ECE9jY5PAwLkZGRkND7axsWGxWI0aHzx4SERubm6NZq6trX3y5EltbW1QUNCPP2757LPPnJycbt6MNzEx0dXVVRyyZ8+eRNSoiioUCokIT+ID/H/27jMuqqNtA/i9hd6WXhUQAREpgghoQBS7iCWxYBILaow9aqpGoylGk1ieRM2rRsQGNqyIFXsDUVFBOihFUHoHt70fNg/PCrqgAmeB6//jgztzZs41u6sxt+ecAWgWKC8CAAAAAPOEQuEXS5bojx6k6eHMdJa3cL/vhLSv1tRrLLkS/XDItCirAVFdfe95fJS3M+y1tcLXjq1Jz3qyYuM99w/v2A9LnPxV6fX6++fKIY1eDvqjBy1cvPi1V6iBPPv55594PN6KFT/ExcVJt58/f37//v3dunUbMWKEg4MDEZ0/f0H6gO3b/yEiJyenN82sqKjo7++/d++eFy+eh4Ud9vX1PXToUN0mMxImJibe3t5Xr15NS0uTtPD5/JCQEFNTU1dX13oTVlVVdetmN3/+/25kzs7ODgsL8/f3JyLZIVVUVAYMGHD37t2UlJS63mPHjhNRnz6ejbxHAADQBHisCQAAAAAw79SpU0/T0513r278ULmRfzCi5km2Zp+e0o2l12MSPl7M1VQ3mDiCxeUWnrqU8f16fmFxpy9nNDpWVFObOPXrl3n5emMGc7U1C09dTpzytd2+9fJfcjX9cnrsBxMjIiJGjhzJdBZ4CyYmJhs2rJ82LdDTs8+sWbNcXHqyWKybN29t3bpVVVV1165gJSWl4cOHOzg4/PXXXzye1pAhQ3Jycg4dOnzy5Ek3Nzc/P796E+bn5wcFBTU8Uc+ezjY2NiYmJvXaly79bsQIv/HjJyxbtlRbW3vt2t/S09PDw0+yWCwi2rZt25w5c1esWL5ixQoejzdgwIDDhw8PHOg7ZsyY1NTUzz6bZWZm9vvvvxFRoyHXrPnV3d1j/PgJq1f/0qlTp4sXL27duvWDDz6QVCcBAOA9obwIAAAAAMwLCQ3V7uuqbGHGdJDGvcx9kbU+qDI2ofJxasPe7I3BJBY7nN6hbG5KRJ2/+/xur9HPtoaaLQpkcdiyx2au2Vqdlmm3Zx1vgAcRGU8f/2DQlNRFP7vcOtzSi3pPyhZm2n1cQkJDUV5sc6ZOnWpoaLhgwcJ169ZJWlgslq+v744d/0juKWaz2ceOHf3kk09Xrly1cuUqyTFjx47988//NNyEJy8v79tvv3vTuZyc6u/0Mnjw4D17ds+YMfPDDz8iIh6Pt379umHDhkl6xWKxUCgUi8WSl0FBOwICJk2fPmP69BlE5OLiEhKyT0NDoykh3dzcTp0KnzYtcPjwEZJef3//nTtfUwkFAIB3wKr7wxoAAAAAgBFisZinq6OzcLLxzAlMZ2lcdcqT9G9/JyJRTW1FbILBpJFWv39b13u/zzjRS4FrzNG6lscTvyi7Hdv78Rm2qrLssXe6D1U0MXS6sKuuJXXhz/mHTzuEb1fv2b011vYecrcfKPrP7pLCItb7bcuzcuXKQ4cOxcfHNX4oNB+xWPz06dOEhAQtLS0HBwdJzU6aSCTKyMhITExUUVGxtbWVbJbSXAQCQUxMjEgkcnd3l2xDLCPno0eP0tPTXVxcJNXPtwrJ5/Pj4uLy8/MdHByMjY2bcQkdWXx8fI8eDnFxcfW28AaADgVXLwIAAAAAw9LT08uKS8x7OTAdpElUrC3swzYTUc2T7Pt969dDdYb2e7Y1tPjiLe0BnkRUnZZZdvOulpcbW1VZ9lhBUamgtFx/wohXzmXViYgqHiTIf3lR3bXHk+KSJ0+eWFpaMp0F3hqLxbKwsLCwsHjTAWw228rKysrKqiXOzuVyPTw8mnIki8VydHR0dKx/FaREoyEVFBR69uz5pl4AAHhnKC8CAAAAAMMyMjKISNmiOa+HYopR4Eel12MSJ3+l0cuBraRYevOeoqFe529mNTqwOu0pESka6ko3Klt1JiJ+QXELpW1GKpZmRJSRkYHyIgAAQEeD8iIAAAAAMKysrIyIOBrqTAdpBlwtdSUzo8r4lIrYBJYCh0QiFpcjrKxqdGDNkxwi4vI0pRuVTI2ISFhW0UJpm5Hk4yspKWE6CAAAALQ2NtMBAAAAAKCjEwgERMTiynrmWlsRN3pO0dlrXX79steDk26PImy3rxaWVyZ8+mVtVq7sgSxFBSISlJRJN4qqa4iIo1X/QXhySPLxST5KAAAA6FBQXgQAAAAAaB7VKU+qEtM0PXsaTh7D1dJgKyvpDO+nP364qLqm6PQV2WMVDXSJqObpM+lGQXEZESno8louMwAAAMB7QnkRAAAAAKB5VCWkEZGm5yt7R/C83YhIUFIue6xyl07EYtVmvlJerHycQkTqPbEfK7Q9AoGgurpaxgEikeidewEAQK6gvAgAAAAA0DxUbCyIqOjUJenGwpMXiUjVrpEtdxUN9TQ9nMtux9Y8zZG0iAWCgqPnFY301R1tWyQuwDsRiUROTs729j2kf0aM8Ks74Ny5cy4urqqqampq6paWXTZt2iRdK0xOTl648AsLC0tdXT0/v5GRkZHSk8vuBQAA+YStXQAAAAAAmoeqjSWvX++SK9EJHy/WGztEuZNx4ekrBcfOq9pa6gz1anS46fzJiZO/TJ613GzBFA5P49nmvTWZz+x2/U4sViuEB2ii7Ozshw8fOjg46Ojo1DXW/ToyMnLo0GE8Hm/atGkKCgqHDx+eP39Bfn7BqlUriai6utrff1ROTs6kSZN0dXXDwsJGjvQ/c+a0t7d3o70AACC3UF4EAAAAAGgmbLb1llUZ328oOHa+5HKUpE3Tw9lq/VKWgkKjo3n9enf9c0Xal78mzVxKRFxNdYsf5vMGeLRsZoC3lJqaSkR79ux2cnJq2PvTTz+LxeI7d6KtrKyI6NdfV5uZdVq3bt2KFcs5HM6yZd8nJSVFRJwaNmwYES1cuMDJyXnq1Gnp6WlEJLsXAADkFsqLAAAAAADvQtnCzDPnRr1GLk/TetMP5stmVyVliGpqVbqaq1h1bnj54WvHEpHeqIG6I/pXPEwkkUi9pz2Lg2cZgdxJSUlhsVg2Njav7c3KyjIzM5PUFolIQ0Ojd+/eV69erampUVNTCw4OdnR0lFQPicjQ0HDIkCG7d++Oiopyd3eX3dsKSwMAgHeDv68AAAAAADQzRWMDno+7zlBvla7mb3trM4vL0XCx1+jlgNoiyKfU1LTOnTtXVFSEh4fv2LHj5s2bQqGwrnfMmDHZ2dkRERGSl0lJSZcuXerfv7+amlpBQUFxcfHAgQOlZ7OxsSaimJgY2b0tvioAAHgPuHoRAAAAAAAAmio1NbWsrMzCwrKqqkrS4urqumfPbjs7OyKaP39eZGSkn9/IPn36KCsrX7p0ycTE5JdffiaipKQkIjI2NpKezdbWlohevMiX3dsaCwMAgHeFfxEFAAAAAACApkpNTS0vL//hhxXJyUk3blz/7LPPYmNjR40aXVlZSUQ8Hs/c3FwsFt+5c+f27dsikYjL5ZaXl9N/H9oovSEMEZmbmxNRSUmJ7N7WWhwAALwLXL0IAAAAAAAATRUcvFNJSalHjx5EZG1t3adPHy0trd9///3IkSOffvqpl5f3o0ePtmzZPHHiRGVl5dOnT8+c+dmIEX7x8XFKSkpEVFRUJD2bpCipra0tu7fVVgcAAO8AVy8CAAAAAABAU7m6ukpqi3WGDx9GRHFx8QkJCY8ePfLx8Zk9e7a2traKisrYsWOnTp1aVVV15MgRIyMjIkpPz5AeK6kn6uvrye5t4TUBAMB7wdWLAAAAANA2PN97XFBUQkQqXS10hvd7pU8kIva7/sP5e4wVVlZz1FRe2yUWCFkc9tvu61I3ViwQsJWV3i1VM74bJVeiKx8kEBFbWcn4s4nvOCe0I1lZWdHR0W5ubp07d65rTE9PJyIDA/2HDx8SUb9+r/z2HDRo4Pr164uLS2xsbFgsluTgOg8ePCQid3d3ExMTGb0ttiAAAGgGKC8CAAAAQNuQu+NgbVaeoqEeb4CHpLxYk56VFxxWdPaasKxCw83R+LMJWh/0auJs7zO28lFS5q//VxGbICgtV9DX0RniZf79XI6GmqS3+OKtrLXbqpMzOBpqmn1djaaM1fRwbjjJ/b4TNPv0tPr9W+nGkivRmav/rkpMFwuFSmZGJrMCjKaMaWKtsCXejYr78fmHzvALilhcLsqLQERFRUUffTTus88+27r1/+oaDxw4SEReXl4qKipEdPjw4ZUrf6jrPXjwEBE5OPQwMTHx9va+evVqWlqalZUVEfH5/JCQEFNTU1dXVxaLJaO3lZcJAABvBTdHAwAAAECboenh3PPGAcufFhGRqKY2cerXL/aH83zcDaeMqc7ISpzyddnt2KbM8z5jKx4kxo9bUPEwSW/MYLMvpnE01J7vPf544kISiYio4Nj5xMlfCcrKTWZ/rD2wb/GFG4lTv65Oy6w3Sf7BiJon2fUaS6/HJHy8uDYr12DiCKMpY0U1tRnfr89aH9TSK5Ix1uyLaT1vHNAZ2q/RSaCDcHR09PT03L59+9Kly2JiYqKjoxcsWHju3LkPP/ywd+/e9vb2gwcPjo+PHzp02N69e69fv75kyZehoaH29vajR48moqVLv+Pz+ePHTzhy5MilS5dGjvRPT0/fvn0bi8VqtBcAAOQWrl4EAAAAgDYpc83W6rRMuz3reAM8iMh4+vgHg6akLvrZ5dbhFh2bt/OwqKbW4dR2NXtrIur01YzHExaWXo8pjLisM8Tr6U+bOarKjmeDuZrqRNR56ey7rqNTZq9wPBdMRC9zX2StD6qMTah8nNpw5uyNwSQWO5zeoWxuSkSdv/v8bq/Rz7aGmi0KZHEauSyAqXcDOhoWi3Xs2NEZM2b++uuvv/76q6Rx9uzZ69b9QURsNjs0NGT+/AWhoaFnz56V9Hp7ewcF7VBUVCSiwYMH79mze8aMmR9++BER8Xi89evXDRs2THKk7F4AAJBbuHoRAAAAANqk/IMRqnZdJRUxIlLQ1+H1c6/NzK24/7hFx5bHxKnZW0tqixIGE0YQUcX9hKrkJy/z8nkDPCW1RSJS0NPm9etdGZ8iLK8gImFFVU16FkdTXd3ZruHML589VzQ2kNQWiYijrqru3F3MF4prX7boit5nLHRABgYGJ04cf/Ik4+zZM1FRt8vKSrds2Sy5LZqIdHR09u3bm5n59MyZ00ePHklIeHz58iXJzc4SEydOLCkpvnXr5o0b1wsK8hcuXCg9uexeAACQT7h6EQAAAADaHkFRqaC0XH/CCOlGFatORFTxIEG9Z/cWGisWCHg+vdWdXzmm9tlzIuLyNF8+LyCiejOo9+xeHHmzKilDo5eDirWFfdhmIqp5kn2/74R6k+sM7fdsa2jxxVvaAzyJqDots+zmXS0vN7aqsoxI77mi9xkLHZm5ubm5ufmbes3MzMzMzN7Uy+VyPTw83q0XAADkEMqLAAAAAND2VKc9JSJFQ13pRmWrzkTELyhuubEsLtfy58XSLfyC4rzgIywuV3tQH5aCAhGVXb9rMiug7oCq5AwikpQXZU9uFPhR6fWYxMlfafRyYCsplt68p2io1/mbWbJHveeK3mcsAAAAAOHmaAAAAABoi2qe5BARl6cp3ahkakREwrKKlhtbT/GFGw98P32Zl2++Yp5qNysVSzN1p26l12NehJwUVlQJyyvygsMKwy8RkWTjF9m4WupKZkYkFlfEJpTfiyORiMXlCCurGh0oJ+8GAAAAdEwoLwIAAABA28NSVCAiQUmZdKOouoaIOFoaLTe2Ts3TnMSpXydO+ZqjrtY9dIPx9HFERGy21fqlCga6aV+tiXEeGeM08snKvwwn+RORio1lo3PGjZ5TdPZal1+/7PXgpNujCNvtq4XllQmfflmbldtyK2qWdwMAAAA6MtwcDQAAAABtj6KBLhHVPH0m3SgoLiMiBV1ey42VyA87m/Hd78RimX8/12j6OLaiQl2Xajcrp8g9hScvViVnKBrqann3Lrt5j4hUbRspL1anPKlKTNP07Gk4eYykRWd4v7I7D3O37S86fcX4s4kttKL3fzcAAACgg0N5EQAAAADaHuUunYjFqs18pShW+TiFiNR72rfcWCIqvnAjdeFPGq49rLesUjI1lO4S8/k1mbkKOloGAX51jc827VE00K1393FDVQlpRKTp2VO6keftlrttv6CkvOVW9J7vBiO2bduWn59PRHZ2dmPHjq1rFwgELBaLw+G8w5wCgYDD4bBYrHfobUgkErHZ73ijWEVFhbq6uowDysvLX758qaurK+OYhuRqCW/SaiEb/aq81RJaLobssXw+v27D7vecud4Szp07d+fOHSJSUVFZvHjxm8cBAPwLN0cDAAAAQNujaKin6eFcdju25mmOpEUsEBQcPa9opK/uaNtyY4ko89etXA01m+2/1KstEpGwujbWOyDj+w11LS9zXxSeuqw92KvRaVVsLIio6NQl6cbCkxeJSNXOquVW9J7vBiP+858/V6/+NTh41+XLVyQt+/bt69Onr4aGprKySrdudps2bRK97mGX1tY2M2d+Vq8xIiLCxcVVXV3D0NBo4sSAq1evNr23nuTk5IULv7CwsNTV1fPzGxkZGdn0Rd27d2/IkKE6OroaGppGRsazZn1eVlbW8LDCwsJu3ey8vLwlLyMjI+3te7z2Z/LkKXKyBLkKKfur0sRPoaVj1HntN/bcuXMuLq6qqmpqauqWll3qjW3izLKXEBUVFRy8a+3a3375ZXXTFwUAHRnKiwAAAADQJpnOnywWCJJnLS+KuFJ6817ilK9rMp9Z/f4tsVhElLv9wO3OXtkbdjbvWEFpeVVSupK5ae7/hT79cZP0T/GFG1xNda2+roXhl17sDxeUllfEJiRO+VrRRN98+dxGl6NqY8nr17sqKSPh48X5YWfLox8+WfVXwbHzqraWOkO9Wm5FjY6VT97e3ikpyX/++R8i2r1796efTi4uLl64cOGcOXMqKirmz1+wenX9skhwcHBqamq9xtDQUD+/kSUlJV999ZWfn194ePjIkf5JSUlN6a2nurra339UUFDQkCFDZs+enZKSMnKkv+wyWZ2YmJgBA3zv3r07adKk5cuXa2lpbdu2beDAQQ0LQ9Onz3j27H+XmrJYLIUGhELh48ePJXUxeViC/ISU/VVp+qfQojHqvPYbGxkZOXTosCdPnkybNm3OnDnV1dXz5y9YterHt5q50SUsX748JSV5zJgxTVkOAAARkRgAAAAAgFEHDhwgIs+cG7J/VGwseP09pFust6xiqypL/lrL1VS3WLWwrst8xTwi6rRk+ptme7ex3Xb99qa/V5t8Pskz54ZLVJiGa4+6RjUH2x4ntzWcp+eNA0RkMGmkdKNb/Gm9MYOli3qaHs49bx5s0RU1OtYz54b+wxYGJgAAIABJREFUR8O4PM1GPyAiOnDgwHt+GX744Yfu3buLxSIZP927dx86dGjdS0dHRxsbm9LSEsnLnJxsRUVFIyMjycusrMwZM2Y4OTlJVjdjxoy6gbW1Naampurq6sXFRZKW58/zFBQUnJ2dG+1t+LNo0SIiiog4JXmZl5draGhoaWkpey2SnylTpigqKt6/f6+uxdfXl4gOHjwgfdiWLZuVlJR0dHTs7OxkzDZv3jxjY+Pc3GdyuARmQ8r+qrzVElouhoxvrFgs6tevHxGlpqZIXpaVlWpqaqqpqQkE/EZnftslTJ48WUdHp9HlxMU9IqK4uLhm+c8BALRRePYiAAAAALRVeqMG6o7oX/EwkUQi9Z72LM7/bs0xmRUgrn2p1NmkecdqD+wrqaO9iZKZUY/j/1eVmFbz9Jmag23DG6gllC3MGs7D5Wlab/rBfNnsqqQMUU2tSldzFavOddXGFlpRo2PlXGlpaVxc3Pz58zU1/326pYmJyYABAy5evMjn8xUUFMrLy5OTk7W0tNzc3CSPk6vz+PHjnJyc8ePH83j/bmJjYGAwePDgU6dOlZaWZmRkyOjV0tKqlyQ4ONjR0XHYsGGSl4aGhkOGDNm9e3dUVJS7u7vsVdy8edPZ2dnZ2bmuZdq0qZGRkdHRd8aNGydpiY+PX7Lky7Vr12zf/o+M213PnDmzZcuW8+fPGRkZxcbGytUSmA3Z6FflrZbQcjFkfGOJKCsry8zMzMrq3wcmaGho9O7d++rVqzU1NQKBQPbMzbUEAICG2szfGwAAAAAAGmJxORou9hq9HOpVxGqeZL/YH67Z26mFxsrMxFK166oz1PtNtUXZFI0NeD7uOkO9VbqaS1/J2NIretNYOcflcq9evfLNN1/XtZSWlj58+HDw4MGSeoqdnd2VK5evXLkcErKv3ljJXca9e7tJN0pexsfHy+6tN1VBQUFxcfHAgQOlG21srIkoJiZG9hL4fP6QIUPmzXvlDvqsrCwi0tHRlrysqakJCJjk5eW1YMECGVMVFhYGBk6fMGHCgAEDGl1gKy+B8ZCyvypvtYSWi0Eyv7FENGbMmOzs7IiICMnLpKSkS5cu9e/fX01NrdGZm2sJAAAN4epFAAAAAGgzKuNTkmct1+jVw3jmBNlH1jzJ6Rb8u6KJwTuc5X3GthxGVvTiwKmSi7crYh+/w0lbh5qaWt++fSW/3rhx49OnmadOnRIKhd99922jYyWXgF28eGnJkiV1jY8fJxBRfHy8t7e3jN4+ffpITyV5UKCxsZF0o62tLRG9eJEvO4aCgsJff/0p3fLixYvNm7coKCj4+f27BflXX3397Nmzs2fPyN5Vee7ceSUlJWvW/NqUBbbyEhgPKfur8lZLaLkYjZo/f15kZKSf38g+ffooKytfunTJxMTkl19+ftuZ32cJAAANobwIAAAAAG0Dr5/7y2fPSSwisbjxg33e/ea+9xnbcphZkVhMYpG6Uze2muo7n73VLFv2fVVVFRHZ29urqKg0ery1tXWvXr0iIyP/+eefCRMmiESivXv3Hjp0iIiEQqHs3npTSbbg0NHRkW40NzcnopKSkrdaRXh4+PTpM/Lz8zdu3ODg4CBp2bRp05EjYcbGxjIGxsfHHzx4cOnSpZ07d27KAltzCfIWstGviowltGaMhng8nrm5eWxs7J07dxQUFEQiEZfLLS8vf9uZm/HjBgAg3BwNAAAAAG2FxcoFNtt+sdn2i/FnE5nO0lEYTPSTvOddNyxjOkvjKisrkpOTgoJ2FBYWurt75OXlyT6ezWYHBe0wMjKaOfMzIyNjIyPjxYuXzJw5k4js7e1l99abSklJiYiKiopezVNJRNra9W+tfZO0tDR//1EjR/pramqeO3dWch90bm7utGmBM2bMaHQb399++11RUXHx4kVNXGCrLUHeQpLMr0qjS2idGG/i5eV9/PjxLVs25+Xl5ue/CAs7XFZWNmKE35MnT95q5uZaAgCABK5eBAAAAACAtkqyYSWb/e9lE9bW1tbW1mw2e+rUaREREYGBgbKHOzg4PHr08ODBg48fJxgbGw0aNOjy5cv038KW7F5pRkZGRJSeniHdKKnd6OvrNWUhe/funT17DovF+u23tQsWLJBUf4jo77//r6CgoLS0dNq0f9eSk5MjFounTQu0sbH+7rvvJI2ZmZkhISEffvhhvevR5GEJdZgN2ZSvSqNLaJ0Yb5KQkPDo0SMfH5/Zs2dLWsaOHXvjxs3169cfOXJk0aJFTZ/5/T9uAABpKC8CAAAAALQGYWU1R61J9z9C061Zs2bp0mWnToUPHz68rlFPT4/+uy+HDC9fvszIyNDT05s+fbrUhGuNjY11dHRk99abysbGhsVipaenSzc+ePCQiJqyCW94ePjkyVM8PT1DQ0Pq7hqW0NfXc3Z2TklJqWupra0ViUSxsbF1hSQi2rZtu0AgmD79lRKSnCxBTkI2+lVpyhJaIYYMDx8+JKJ+/fpJNw4aNHD9+vXFxSVvNfN7ftwAAPXg5mgAAAAAgBZU+SgpYdKiO92HRtsMjHEemf7Nb8LySqZDtR+S5+KdP39BunH79n+IyMmpkb2/q6qqunWzmz//f3e/Zmdnh4WF+fv7N9pbj4mJibe399WrV9PS0iQtfD4/JCTE1NTU1dW10VUsXbpMS0vr8OFDData8+fPv3//nvSPnZ1dly5d7t+/t2PHP3WHnTt3TkdHx9fXt+kLbLUlyEnIRr8qTVlCK8SQoXv37kR0+PBh6caDBw8RkYNDj7ea+T0/bgCAejgrV65kOgMAAAAAdGjx8fGHDx/utGR644e2NRUPEh+PX8jPL9IbPYjn41HzJLv0SnTp9RjDAD+SuQVwW5S9PmjcuHENb2h9K5cvX378+PHcuXNkHLNlyxYtLa1PPvmYiLp27Xr06NFTp06JxWJlZeWoqKgff/zp6NGjvXr1Wrt2rfT1fUVFRX/99ZeLi4u//0hJi7Ky8rVr186cOWNqamphYREXFzdp0sdKSkpHjoQpKSnJ7iWibdu2ubt70H+vJjMxMdmzZ+/Vq9f09fXy8vLmzp139+7d/ftDra2tiWjDhg1eXt7U4NIzIiouLv7yyy+7d++em5t37tx56Z+XL1/a2NjUO37btm0CgWDu3LnSMyxatGjQoEEBAQHSR8rVEhgPKfurUlpaKnsJrRND9jdWX1//9n8RUVlZ2X/+8+fOnTttbW03bNhga2sre+a3WgIRHTt2LDk5+ZtvviaZ8vPzt2z5e86cOQYG77KvPQC0D7g5GgAAAACgpeTtPCyqqXU4tV3N3pqIOn014/GEhaXXYwojLuv6DWA6XXvAZrOPHTv6ySefrly5auXKVZLGsWPH/vnnf7jcxv9nJyhoR0DApOnTZ0yfPoOIXFxcQkL2aWhoNKVXLBYLhULxf/cxHzx48J49u2fMmPnhhx8REY/HW79+3bBhwyS9IpFI+mBpN27cEIvF9+7du3fvXr0uFovl5+fX6CouXbokEok8PT3edoGtuQTGQ8r+qjS6hNaJ0fD4emNDQ0Pmz18QGhp69uxZSaO3t3dQ0A5FRUUikj3zWy0BAOCtsF77Rx4AAAAAQKs5ePDghAkTPHNuMB2k+d3/YCJXS8Ph1Pa6loIj51LmrzL5fJL58rkyBrZFt0z7HjhwYPz48e8zycqVKw8dOhQfHyfjGHv7Hp07dz59OqKuRSQSZWRkJCYmqqio2NrampqaNv2MYrH40aNH6enpLi4uDe+Kld3bkEAgiImJEYlE7u7uHA5HuuuXX37p0qVLvWv3WkGbWEKrhXyfr4r8xMjOzo6Pj6+uru7WrZutrS1L6lLot51ZxhKmTJkaHh5eWFgge4b4+PgePRzi4uLe88plAGjTcPUiAAAAAECLEAsEPJ/e6s7dpRtrnz0nIi5Pk6FQ7RObzbaysrKysnqHsSwWy9HR0dHR8R16G+JyuR4er7lALzU1NSho5+XLl94h4XtqE0totZDv81WRnxhmZmZmZmbNMvOblgAA8FZQXgQAAAAAhv17455QxOK0q40HWVyu5c+LpVv4BcV5wUdYXK72oD5MpWohYoGQ/vtRtoLY2Njx4yd4enosWrSodc74ntLS0k6ePNGpUyemg7y7NrEEOQkpJzHe2c6dO0+fPhMdHc10EABoM1BeBAAAAACGaWlpEZGwvKJ9X9NXfOFG2pJf+YUlFqsWqnZrnoun5IewvIKIeDxeK5xr8ODBWVlZIpGoDT3oaciQIUxHeF9tYglyElJOYrwzsVgsEol69epV9wRMAADZUF4EAAAAAIZZWloSUXV6loZL+3x0V83TnCc//Kf4/A1lCzPrTT9oebkxnaj5VadlElGXLl1a4VwbNqxvhbMAdFiBgYGBgYFMpwCAtgTlRQAAAABgmKWlpaY2r+JuXLssL+aHnc347ndiscy/n2s0fRxbUYHpRC2i4v5jTW2eubk500EAAACgtbWrp9sAAAAAQFvEYrGGDRladr4d7hxdfOFG6sKfVO26Ol3cazJ7UnutLRJR6bnrw4cOk97BFqAekUjEdAQAAGgRKC8CAAAAAPMmBQQU37xX8ySb6SDNLPPXrVwNNZvtvyiZGjKdpQXVZGSV3Lo/KSCA6SAgj5KTkxcu/MLCwlJXV8/Pb2RkZCTTiQAAoJnh5mgAAAAAYN6IESPMu3TJ+WOH1aYfmM7SbASl5VVJ6Wo9bHL/L7Rel2afntoD+zKSqiXkrAsy79Jl+PDhTAcBuVNdXe3vPyonJ2fSpEm6urphYWEjR/qfOXPa29ub6WgAANBsUF4EAAAAAOZxOJz/rF8/evRo/U9GaXo4Mx2neZTfeUhiceWjpMpHSfX7WKx2U14sj3mUf+z88ePHORwO01lA7ixb9n1SUlJExKlhw4YR0cKFC5ycnKdOnZaensZ0NAAAaDYoLwIAAACAXPD39/cdPCj6h//YndjKVlJkOk4z0B7Y1zOnHT5QUpqo9mXmsvW+gweNHDmS6Swgj4KDgx0dHSW1RSIyNDQcMmTI7t27o6Ki3N3dmc0GAADNBc9eBAAAAAB58femzZTzImPxahKLmc4CTSAWpy9eTTkv/t60mekoII8KCgqKi4sHDhwo3WhjY01EMTExDIUCAIDmh/IiAAAAAMiLrl27Hjl0uOjU5az1QUxngcZlrQ8qOnX5yKHDXbt2ba45uVyuUChsrtmAWUlJSURkbGwk3Whra0tEL17kM5MJmpvkNyyXizsjATo0lBcBAAAAQI74+vpu2bw5Z8POp6v+EgtFTMeB1xMLRU9X/ZWzYeffmzf7+vo248xaWlqlpaXNOCEwKDU1lYh0dHSkG83NzYmopKSEmUzQ3CQfJY/HYzoIADAJ/8IAAAAAAPJl5syZ6urqUwOnvczI7vLXCo6GGtOJ4BXC8sq0+T+WX7uzb9++gICA5p3c0tLy+fPnJSUlqFa0A0pKSkRUVFQk3VhZWUlE2trazGSC5paUlKSmpqavr890EABgEq5eBAAAAAC5ExAQcPniJfHD5Ef9JuUfOo1HMcoLsTj/0OlH/SbRw+TLFy81e22RiFxcXMRicVRUVLPPDK3PyMiIiNLTM6QbJdVGfX09ZjJBc4uKinZ2dmazUVsA6NDwRwAAAAAAyCNPT8/kxwlTPxyfvuTXxyNnFZ68KObzmQ7VcYn5/MKTFx+PnJW+5NepH45Pfpzg6enZEicyMTFxdHQ8eTK8JSaHVmZjY8NisdLT06UbHzx4SETYNrp9EAqFERERQ4cOZToIADCMJcY/BQMAAACAHHv48OH3K5afCj/FVVHW6Ouq2sNa0diAo6HKdK4OQVhe9TL3RVVcSvmNu4LqmhF+I37+8SdHR8cWPemaNWt+++237OwsVVV8ym2ej0//6OjoR48eWllZERGfz+/e3b66ujorK5PFYjGdDt7XiRMnxowZm5ycLPl8AaDDQnkRAAAAANqA7OzsEydORF68eO/Bg/wXzyvLyplO1CGoaqjrGxi6Ojv7DhgwatQoU1PTVjhpfn5+ly5dvv32m2XLlrXC6aBFnTt3bsQIP0dHx2XLlmpra69d+9v58+fDw08OGzaM6WjwvoRCoZtbb1NT05MnTzKdBQAYhvIiAAAAAADIlzVr1vz000+PH8dLdhmGNm3//v0zZsyU7OjC4/FWrvxh4cKFTIeCZrB169Z58+bHxsba29sznQUAGIbyIgAAAAAAyJfa2toePXo4OjoePnwIt9C2AwKBICYmRiQSubu7czgcpuNAM8jLy3N0dPr000/XrVvHdBYAYB7KiwAAAAAAIHcuXbo0ePDgFSuWL1++nOksAPCK6upqH5/+JSUl0dHRWlpaTMcBAOZxmQ4AAAAAAABQX//+/f/66685c+ZYW1tPnDiR6TgA8C+RSDR16rTU1NTbt2+jtggAEigvAgAAAACAPPr888+Tk5M//XRycXHx7NmzmY4DAFRZWfnJJ5+ePn36zJkz1tbWTMcBAHmB8iIAAAAAAMipdevW6erqzp07LzEx6Y8/fldQUGA6EUDHlZWVNXr0mKysrMjIyL59+zIdBwDkCJvpAAAAAAAAAK/HYrGWLVt24MCBf/75x8nJ+dy5c0wnAuiIamtr16xZ0727fW1tbVRUFGqLAFAPyosAAAAAACDXxo0bFxcXZ2dnN2TIUD+/kefPnxeJREyHAugQysrKtm7d2qOHw88///LNN9/ExMRYWloyHQoA5A52jgYAAAAAgLYhMjJy1apV165d09fX9/X1dXZ2MjQ0VFNTYzoXQHtTVFT05MnTmJiYa9eusdnsgICAVatWmZmZMZ0LAOQUyosAAAAAANCWJCYmHj9+/Nq1a3FxcQUFBZWVlUwnAmhveDxep06dnJycBg8e7Ofnp62tzXQiAJBrKC8CAAAAAAB0OBEREf7+/rt27fr444+ZztL8Ll++PGDAgK1bt86cOZPpLAAA7R/KiwAAAAAAAB1LUlKSh4fHyJEjd+/ezXSWlrJixYo1a9acO3fOx8eH6SwAAO0cyosAAAAAAAAdSHl5uYeHh6am5uXLl5WUlJiO01LEYvHEiRMjIyOjoqKsrKyYjgMA0J6hvAgAAAAAANBRiESiUaNG3b17986dO6ampkzHaVnV1dVeXl7V1dW3bt3S1NRkOg4AQLvFZjoAAAAAAAAAtJLvvvvuwoULR48ebfe1RSJSUVEJCwsrKCiYMmWKSCRiOg4AQLuF8iIAAAAAAECHcPjw4d9//33Tpk3u7u5MZ2kl5ubmR44cOX369MqVK5nOAgDQbuHmaAAAAAAAgPbv/v37H3zwweeff75u3Tqms7S24ODgwMDAffv2BQQEMJ0FAKAdQnkRAAAAAACgnXv+/Lmbm5utre3p06e5XC7TcRiwcOHC7du3X7lyxc3NjeksAADtDcqLAAAAAAAA7Rmfzx80aFBmZmZ0dLSenh7TcZghFAr9/f1jY2Ojo6M7wnMnAQBaE569CAAAAAAA0J7Nmzfv3r17J06c6LC1RSLicDh79+5VU1MbN25cbW0t03EAANoVlBcBAAAAAADarc2bN2/fvj0oKKhHjx5MZ2GYtrb2yZMnExISZs6cyXQWAIB2BeVFAAAAAACA9un69euLFy/+8ccfP/roI6azyAVbW9sDBw6EhIR0wP1tAABaDp69CAAAAAAA0A49ffrUzc3Ny8vr8OHDLBaL6ThyZN26dV9//fXx48f9/PyYzgIA0B6gvAgAAAAAANDeVFdXe3l5CQSCGzduqKmpMR1H7sycOfPAgQO3bt2yt7dnOgsAQJuH8iIAAAAAAEC7IhaLAwICLly4EB0d3aVLF6bjyCM+nz9w4MCsrKyOvJs2AEBzwbMXAQAAAAAA2pWff/75yJEjhw4dQm3xTRQUFA4ePCgQCAICAgQCAdNxAADaNpQXAQAAAAAA2o/jx4+vXLly48aN/fv3ZzqLXDM0NDxx4sStW7eWLFnCdBYAgLYNN0cDAAAAAAC0E4mJiR4eHmPGjNm5cyfTWdqGI0eOfPTRR3///fesWbOYzgIA0FahvAgAAAAAANAeFBcX9+7d28DA4OLFi0pKSkzHaTNWrFixZs2ac+fO+fj4MJ0FAKBNQnkRAAAAAACgzRMKhSNHjnzw4MGdO3dMTEyYjtOWiMXiiRMnRkZGYiccAIB3g/IiAAAAAABAm7d48eK///776tWrbm5uTGdpe6qrq728vKqrq2/duqWpqcl0HACANgZbuwAAAAAAALRte/bs2bhx444dO1BbfDcqKiphYWEFBQVTpkwRiURMxwEAaGNQXgQAAAAAAGjD7t27N2vWrK+//nrSpElMZ2nDzM3Njxw5cvr06ZUrVzKdBQCgjcHN0QAAAAAAAG1VXl6em5tb9+7dIyIiOBwO03HavODg4MDAwJCQkIkTJzKdBQCgzUB5EQAAAAAAoE2qra318fEpKiqKiori8XhMx2knFi5cuH379itXruBOcwCAJkJ5EQAAAAAAoE2aOXPmgQMHbt26ZW9vz3SW9qNuD+7o6GhTU1Om4wAAtAF49iIAAAAAAEDbs3HjxqCgoJCQENQWmxeHw9m3b5+amtq4ceNqa2uZjgMA0AagvAgAAAAAANDGREZGfvXVV6tXr/bz82M6Szukra198uTJhISEmTNnMp0FAKANwM3RAAAAAAAAbcmTJ0/c3Nx8fHwOHjzIYrGYjtNunTt3bvjw4WvXrl2yZAnTWQAA5BrKiwAAAAAAAG1GRUVFnz59OBzOjRs3VFVVmY7Tzv3xxx/ffPPN8ePHcZUoAIAMKC8CAAAAAADIo/Lycg0NDekWsVg8YcKEq1ev3rlzp1OnTkwF61DetH9OVVUVyrsAABJ49iIAAAAAAIA8Wr16dUBAQHV1dV3LypUrjx07duDAAdQWW83mzZt79uw5cuTIgoKCusZr1645OjpWVVUxGAwAQH6gvAgAAAAAACB3RCJRcHDw/v37PTw8srOziejo0aM//fTTX3/91a9fP6bTdSCKiooHDx4UCAQBAQECgYCItm/fPmDAgLS0tKNHjzKdDgBALuDmaAAAAAAAALlz6dKlAQMGEJGCgoKamtrGjRsXLFjw6aefbtq0ieloHVFsbOwHH3wQGBioqqq6du1aIuJwOD4+PhcuXGA6GgAA81BeBAAAAAAAkDvTp0/fs2cPn88nIg6HIxQKu3bt+vjxYwUFBaajdVBBQUHTp09ns9kikUjSwmKxMjMzzczMmA0GAMA43BwNAAAAAAAgX2pqag4ePCipLRKRUCgkorS0tLlz59Y1QmtKSUlZvXo1h8Opqy0SEZfLDQ0NZTAVAICcQHkRAAAAAABAvpw4caKysrJeo1gs3rlzp7e39/PnzxlJ1WGdOXPGxcUlMzNTUuetw+fzt2/fzlQqAAD5gfIiAAAAAACAfNm9ezeHw2nYLhAIoqKievfunZ6e3vqpOqb169ePGDGisrLytdeNpqSk3Lt3r/VTAQDIFZQXAQAAAAAA5EhBQcHZs2clmxTXo6CgoKmpuXz5cktLy9YP1jENGjSod+/eRMRisRr2Kigo7Nmzp9VDAQDIF5QXAQAAAAAA5MiBAwca7sApuZhx/PjxKSkpM2bMeG2pC1qCg4PDzZs3g4ODtbW1uVxuvV4+n79r1y48EBMAOjiUFwEAAAAAAOTIzp0765UXuVxu586dL1y4sHfvXn19faaCdVgsFmvy5MlpaWmzZ89ms9n1Nu8uKSk5e/YsU9kAAOQByosAAAAAAADyIi0t7d69e3XbEysoKCgpKS1btiwxMdHX15fZbB0cj8f7888/Y2JinJyc2Gx23QWkHA5n165dzGYDAGAWyosAAAAAAADyYs+ePZI7cNlsNhH5+vomJiauXLlSUVGR6WhARNSzZ8/o6OidO3dqampKLmMUCATHjx8vKSlhOhoAAGNQXgQAAAAAAJALYrE4ODiYz+dzuVxDQ8Njx46dPn3awsKC6VzwCsm90ikpKQEBASwWi8Ph8Pn8gwcPMp0LAIAxKC8CAAAAAADIhdu3bz99+pTD4SxevDglJWXUqFFMJ4I30tfX37Vr19WrV21sbIhox44dTCcCAGAMyosAAAAAAAByYe/evX379o2NjV27dq2amhrTcaBxH3zwwcOHD9evX5+YmJiWlsZ0HAAAZrDqbUkGAAAAAAAdU1FRUXx8fHFxcW1tLdNZOqiYmBhXV9e6PUPkDZvN5vF4lpaWlpaWchuyHrFYnJGRkZGRUVxc3KL/81tcXFxQUGBtbd1yp2j3lJSUtLW17e3tdXR0mM4CAG+Hy3QAAAAAAABgUnx8fFBQUHh4eHJyMtNZoG3Q1tYeMmTIpEmThg8fzuFwmI7zGkKh8NSpUyGhIWfOni4tLmM6DrwdK+suo/3HTJs2zd7enuksANAkuHoRAAAAAKCDSk1NXbJkyYkTJ7p27Tp27Nj+Pj4ODg56enpKSkpMRwN5JBKJioqKUlNTb926dTI8/PLly1ZWVuvWrfP392c62itOnDjxxeKFT9Kfdumjaz1Ip5Orlq6lqgqPy2K3jSsuOyzBS1FVEf95QkXGjaKk08X5GWV+/n4b1m3o2rUr09EAoBEoLwIAAAAAdDg1NTWrVq3asGGDtbX1b2vXDh06tK3c6wryIzU19YeVK0NDQwcNGrR582Z5qAGlpqbOmTv7wvlIxzHG/b+00LFQZToRvCOxmFIvFV74Ob0wo2rxoiU//PCDsrIy06EA4I1QXgQAAAAA6FhevHgxevTohISEn3788fPPP+dy8cQkeHfXr1+fv2DB06dPDx065Ovry2CSyMjID8eNVTdlD/25q3lvHoNJoLmIBOI7e7Iv/fbEobvTiWMnDQwMmE4EAK+H8iIAAAAAQAcSHx/v5+fH5XJPnjjRrVs3puNAe1BTUxM4ffrhw4c3b948c+ZMRjJs3759ztw59n4Go9Z34yqxGckALaQgtTJ0SpyySOuAV6NXAAAgAElEQVT0qTN4GiOAfEJ5EQAAAACgo8jKynJ3d7eysjp29Kiuri7TcaD9EIvFq1at+vGnn/bt2xcQENDKZw8NDf344499Flv6LO6Cu/zbpapi/v7AuOpMzp2omE6dOjEdBwDqQ3kRAAAAAKBDqKqq8vHxqaiouHnjBo+HW0eh+S358sstW7ZcvHjR09Oz1U4aExPj1c/LdbLhkBXWrXZSaH21FcKgUff0FDvdvH5LXV2d6TgA8AqUFwEAAAAAOoRJkyZFRkZG3b5tYWHBdBZon0Qi0egxY6Kjo+Pj41vn8tjCwkI7+256TpyJQT2wMXS7V5JVvX3EvRGD/END9jOdBQBegWdSAAAAAAC0f5cvXw4NDd0ZFITaIrQcNpu9d88eNpu9YsWK1jnjihUrXlLV2L/sUFvsCHidVEZtsN0feuDy5ctMZwGAV+DqRQAAAACAdk4oFLq4uJibm584fpzpLK+RmJh448YN6RYOh2NhYeHq6qqhoVHv4Ojo6GvXrt27f7+ystLGxsanX7/hw4e/aeby8vKXL1+2wmV0IpGIzW7qpRsVFRXt+9bOXbt2TZ8x4+7du05OTi16ovj4eCdnp1HrujmPM27RExFRQWpl5p1S6RYWm3idVEwcNZXUOfUOzr5f9jSqOPdR+csqoZ6VqoWnto2v3ptmrq0QCvkiVW2F5g0sFombWHIVCcTEIjbn9QeLBGIWh/WmJ1rKHttCQqY8pBztRw/isOs9gPxAeREAAAAAoJ3bu3dvYGBgfFyctbU8Ppxu69atn8+e3bBdX19/29ato0ePlrwUCATfLV36xx9/EJG2trZYLC4pKSGioUOHhuzbp62tXW94YWGho5OTlpbW4/j4FkqenJy8ecuW48ePl5aW9u3bd9EXX/j6+r7p4Hv37n23dOmdO3eKi4sNDQ1HjRr1+2+/aWpq1jvM2sbGx8dn+7ZtDWeQ0SVXxGKxh6enkZHR8RYuZ48cNfJh1s3p4S6tsJ3LnT05J79JaNiupqvo/7ud3VB9yUuRQHz+19Qbfz8lIhUtBbFYXFMmICLr/rofbemholW/hlhVzN884LayJnf+leZ5WmVhelXUzqzEs/k1ZYLOvXl9Puvc5QOdNx388Ehe1M6s3LhykVCsY67iHtip9xSzuqJkcmRB5Nq0/ORKJQ2uZV/t3lPNLDy0mzi2RRVmVG3yiQoOCv7kk09a4XQA0BS4ORoAAAAAoJ37v//7v9GjR8tnbbHO8uXL4x49kvxcunhx7Zo1ZWVln3z6aXp6uuSA8RMm/PHHHz4+PokJCUWFhfkvXly+dOmTTz45c+bM5ClTGl42MX3GjGfPnrVc4Orqav9Ro4KCgoYMGTJ79uyUlJSR/v5Xr1597cExMTEDfH3v3r07adKk5cuXa2lpbdu2beCgQSKRSPqw4ODg1NTU184go0vesFisJYsXnzp1Kjs7u+XOkp2dHXEqwvNz09bcKtpnkeW8S56Sn2mHXQd/b11bLgibF1f8tFpywIFZj278/dSyj/aCa32+S+j3bVy/wDBXpw+NUy4VHlkQ3/DanmOLH5c/r22uePwa0b4psff2P+vqo9t7illhetW+ybFPbhe/9uDYQ7lh8+OqSwWeMzr3nmL2slJ4alnS1T+fSHofHcvbNzm2pkzQd465zUC95AsF+6Y8KEirasrYlqZrqWo3VP/vrZtb53QA0BS4lhgAAAAAoD3Ly8u7devWsaNHmQ7SCBNjY3t7+7qXPj4+IpHou6VLT548uXDhwosXLx49erR///4Xzp+X3IbM5XL79evXp0+fmJiY8PDw69eve3l51Q3/+++/z5w5o6Pzxuu23t+y779PSkqKOHVq2LBhRLRwwQInZ+ep06alp6U1PHjT5s3V1dVRt287OzsT0Y+rVg0cNCgyMjIsLGzcuHHZ2dmrfvzxzp07Dx48qDdQRpc8Gz16tKqq6okTJ+bMmdNCpzh+/LiSqkK3/1422Do0DJUMbNX++0rNso+2WCQ+vzo18Xy+54zO6deLEk6/sOyrPfWAi+Q6PjaXZeGp3akXL+dBWdL5gszoEnP3/23aHr0rO/VSoQqv2W6LjlyTWpBW9enentYDdInIY0bnLQNvH/3i8aLbfRsefOP/nupYqs465aakwSUir3kW63vfiArO6veFpZAvOvtjioIqZ/Y5d2VNLhENXtb1D9drBz9/NOe8u+yxzbUW2Rw/NNwfGPX8+XNDQ8PWOSMAyIarFwEAAAAA2rPLly9zOBwZN+3KLRsbGyLKzMoiop9+/pmIVv/yS71HHCooKGzbunXy5MmSG6Ul4uPjl3z55do1a4yN3/qRfM+fP9+8efOdO3caPTI4ONjR0VFSWyQiQ0PDIUOGZGRkREVFNTz45s2bzs7OktqixLSpU4ko+s4dIiovL09OTtbS0nJzc6s3UEaXPFNUVBwwYMDFixdb7hQXL1207MvjKDD8v7S6XVSJqDSnhoiubMwgooHfdq13jzBHgTXqdzvnccY1Zfy6xhdJlWdXJQ/6vquGoeLbnrQi/2XUzqyc2LJ67fcP5BraqUtqi0Skrq/Y1Ue3OLM6+15pvSNrygQvkiptBuhJ6oNEpGGo1OUD7epigZAvzk+uLMurtfHVk9QWiUhNT9Gqn25efHlNmUD22Lddy7ux8tJhcdjY4AVAfuDqRQAAAACA9uzhw4e2traqqqpMB3lrofv3E5FVly5EdP/+fQMDAw8Pj4aHeXl5SV+3WFNTEzBpkpeX14IFC7b/808Tz1VQUBAWFnbw0KErV64IhcKww4dll/MKCgqKi4unTZsm3WhjbU1EMTEx7u7u0u18Pn/IkCG9X50wKyuLiHS0tYnIzs7uyuXLRJSammptYyN9mIwuOdfT2TkkNLTl5o99cM98lFrjx7WwR8fyiEjHXJWIcuPK1fQUO7lqNTzM3J0nfd2ioFZ0aM4jc3dtj+md7+7LaeK5qor48adexJ98/uRWsUgonviPo6mzpnRvdSm/58RXSuqS6mfOgzIzl1dSsbms6UddtTur1LXUlAnyHld09dHhKLDK8mqJSHpyIjJz1ky+UPAiudKou7qMsU1cy3tSUOEYWmk+evRowoQJrXNGAJAN5UUAAAAAgPYsNze3U6dOTKdoXFZ29v379yW/fvLkSeTFi8eOHdPW1p4wYUJ+fn5paWnv3r2bMs9XX3/97Nmzs2fOvHGzWynFxcVHjhw5eOjQxYsXBQKBk5PTsmXLRvn7u7i4yB6YlJRERMZGRtKNtra2RPQiP7/ewQoKCn/9+ad0y4sXLzZv2aKgoODn59eURbVFZmZmubm5LTd/bu5zR9NWuhW3Tumz2ty4csmvS7Kq068XJ5zJV9FS6DHKsLLwZU2ZwKxn/b16XuvsjynlebWTQ3o25cGR1aX8hIj8uBPP028UiQRio+4a3gst7YboGzu8sq96QVolEWkYKEk36lmpEVFlIZ9epajK6ez2b7nz1vbMkuya5AsFYpHYa74lEelYqBJRxo3ivp+b1w15kVxJRPlJFZ17ackY22rUjRVa9AsGAG8F5UUAAAAAgPasqqpKTY35i7watXr16tWrV0u3mJqa7t2zR1dX9+nTp0RkYGDQ6CTh4eGbNm06EhbW6G3Rz58/D5w+/fz582Kx2Nvbe90ff4waNcrc3Py1B4tEourq6rqXysrKkl1W6j3bUTJc+jbtN4WcPmNGfn7+xg0bHBwcGl1UG6Wurl5RUdFy89dU1Siqclpu/te6+mfG1T8zpFs0jZQ+3NRDVVuhJLuGiNT0Gr/TOel8QdTOrIAdjhqGSrKPrMh/eWzx47SrhWIxWXhoD/3BptsQfZ6Z8msPLsqoJiIV7Vee5Cg5uKa0fnlR2oU1afxqIREZ2KopKLOJSNdSxdRJM/1a0d2QnB7+RmKR+EFYbnz4CyISCcWyx7YaBTVWi37BAOCtoLwIAAAAANCeicXiplzHx7jp06cPHjRI8msOh9OlSxc7OztlZWUi6tSpk7KycqPbQOfm5k4LDJwxY8aYMWMaPd2LFy8iIiK4XO68efOmTZ3q6Ogo4+CoqKg+ff+3OUbIvn1KSkpEVFRUJH1YZWUlEWlra79pnrS0tEWLF588ebJr16779u4dOHBgoznbLhaL1XA772bUopO/iUuASdd+/z7ZkM1haZur6FurcZXYRKRlosRVYktuK5ah/Hnt0UXxrpNM7YY1Xi6vLHiZHFnA5rLcp3VymWhiaKcu42COEpuIqotfqSS+rBISkezdY5an9S/MqMqMLjn/a+q2EdFL7nipGyiOXt9935TY418mRCx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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "from IPython.display import Image  \n",
    "import graphviz\n",
    "\n",
    "class_names = list(map(str, set(y_train)))\n",
    "df_X_train_pca = pd.DataFrame(X_train_pca)\n",
    "tree = rf.estimators_[0]\n",
    "dot_data = export_graphviz(\n",
    "    tree,\n",
    "    out_file=None, \n",
    "    feature_names=['PC1', 'PC2', 'PC3', 'PC4'],\n",
    "    class_names=class_names,  # Use the list of class names\n",
    "    filled=True,\n",
    "    rounded=True,  \n",
    "    special_characters=True,\n",
    "    label='root',\n",
    "    proportion=False,  # Set proportion to False\n",
    "    impurity=False\n",
    ")\n",
    "graph = graphviz.Source(dot_data)\n",
    "Image(graph.render(format='png'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing the Random Forest model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.22 Jupyter notebook code — Scale the features and apply PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cea4a5c7-153b-4fe5-ba2e-352b658cc771",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled = scaler.transform(X_test)\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)\n",
    "X_test_pca = pca.transform(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.23 Jupyter notebook code — Features reduction using PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3262d5aa-4097-4c3f-81eb-581f6c9ce6f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf.predict(X_test_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.24 Jupyter notebook code — Evaluate the Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f49b3141-147f-4fc8-a477-6cf100d94251",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "F1 Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy:', accuracy)\n",
    "\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "print('F1 Score:', f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3672, 4)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_pca.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.26 Jupyter notebook code — ML model Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d1220b39-7bcf-40d2-9ed1-d660789fbe14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[835   0   0   0   0]\n",
      " [  0 601   0   0   0]\n",
      " [  0   0 817   0   0]\n",
      " [  0   0   0 671   0]\n",
      " [  0   0   0   0 748]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print('Confusion Matrix:')\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3f05b39f-fc40-4d88-ad4e-43d3bf208fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "sns.heatmap(pd.DataFrame(confusion_matrix(y_test, y_pred)), annot=True, fmt='g')\n",
    "ax.set_ylabel('Actual Label')\n",
    "ax.set_xlabel('Predicted Label')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the XGBoost model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Listing 9.42 Jupyter notebook code — Train the XGBoost using X_train_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "51d347de-8165-4d73-afb4-b711cbc65a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, objective='multi:softprob', ...)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgb = XGBClassifier(random_state=42)\n",
    "# Add silent=True to avoid printing out updates with each cycle\n",
    "#xgb.fit(X_train_pca, y_train)\n",
    "xgb.fit(X_train_pca, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the XGBoost model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.43 Jupyter notebook code — Evaluate the XGBoost model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "429660c5-ff7d-4c06-b136-292fd33b5b52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error : 0.0\n"
     ]
    }
   ],
   "source": [
    "#y_pred = xgb.predict(X_test_pca).round()\n",
    "y_pred = xgb.predict(X_test_pca)\n",
    "#y_pred = y_pred.round().astype(int)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"Mean Absolute Error : \" + str(mean_absolute_error(y_pred, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "04cfa5b5-7028-4785-b221-fb392a8c6dcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 0, ..., 1, 0, 4])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f65476a3-2009-4648-aaf5-af23c746812c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "F1 Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "# calculate and print the accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy:', accuracy)\n",
    "\n",
    "# calculate and print the F1 score\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "print('F1 Score:', f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.45 Jupyter notebook code — Generate the XGBoost ML model Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9560b579-5d31-4ada-8981-986dc7484da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "sns.heatmap(pd.DataFrame(confusion_matrix(y_test, y_pred)), annot=True, fmt='g')\n",
    "ax.set_ylabel('Actual Label')\n",
    "ax.set_xlabel('Predicted Label')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f65e22-3be4-4bcd-ae52-ac6a609be7ca",
   "metadata": {},
   "source": [
    "## Answers to Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.47 Jupyter notebook code — Decision Tree with 1000 data points from df_orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import math\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "\n",
    "\n",
    "path_train = r\"dtqbc-m-train.csv\"\n",
    "df_orig = pd.read_csv(path_train, usecols = ['label','qname', 'qd_qtype'])\n",
    "df_orig.rename(columns={'qname': 'fqdn', 'qd_qtype':'query_type', 'label':'class'}, inplace=True)\n",
    "df_orig = df_orig.drop_duplicates()\n",
    "df_orig = df_orig.reset_index(drop=True)\n",
    "df_orig = df_orig.sample(n = 1000)\n",
    "\n",
    "df_orig['fqdn_length'] = df_orig['fqdn'].str.len()\n",
    "df_orig['fqdn_count_numbers'] = df_orig['fqdn'].str.count('\\d')\n",
    "df_orig['fqdn_count_letters'] = df_orig['fqdn'].str.count('[a-zA-Z]')\n",
    "df_orig['fqdn_count_special'] = df_orig['fqdn'].str.count('[^\\d\\w\\s]')\n",
    "df_orig['fqdn_count_lower'] = df_orig['fqdn'].str.count('[a-z]')\n",
    "df_orig['fqdn_count_upper'] = df_orig['fqdn'].str.count('[A-Z]')\n",
    "df_orig['fqdn_count_num_fqdn_labels'] = df_orig['fqdn'].str.count('\\.')\n",
    "\n",
    "\n",
    "def firstlength(string):\n",
    "    return len(re.findall(\"[^.]+\", string)[0])\n",
    "\n",
    "def extract_labels(string):\n",
    "    return re.findall(\"[^.]+\", string)\n",
    "\n",
    "def maxlength(list):\n",
    "    return len(max(list, key=len))\n",
    "\n",
    "def entropy(s):\n",
    "    p, lns = Counter(s), float(len(s))\n",
    "    return -sum( count/lns * math.log(count/lns, 2) for count in p.values())\n",
    "\n",
    "df_orig['first_fqdn_label_length'] = [firstlength(x) for x in df_orig['fqdn']]\n",
    "\n",
    "df_orig['extracted_fqdn_labels'] = [extract_labels(x) for x in df_orig['fqdn']]\n",
    "\n",
    "df_orig['max_fqdn_label_length'] = [maxlength(x) for x in df_orig['extracted_fqdn_labels']]\n",
    "\n",
    "df_orig = df_orig.drop(['extracted_fqdn_labels'], axis='columns')\n",
    "\n",
    "df_orig['fqdn_entropy'] = [entropy(x) for x in df_orig['fqdn']]\n",
    "\n",
    "df_orig_selected = df_orig[['class', 'query_type', 'fqdn_length', 'fqdn_count_numbers', 'fqdn_count_letters', 'fqdn_count_special', 'fqdn_count_lower', 'fqdn_count_upper', 'fqdn_count_num_fqdn_labels', 'fqdn_entropy', 'first_fqdn_label_length','max_fqdn_label_length']]\n",
    "\n",
    "X = df_orig_selected.drop('class', axis=1)\n",
    "y = df_orig_selected['class']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=X_train.columns)\n",
    "X_train_scaled\n",
    "\n",
    "pca = PCA(n_components=4)\n",
    "fit= pca.fit(X_train_scaled)\n",
    "X_train_pca = pca.transform(X_train_scaled)\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train_pca, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.48 Jupyter notebook code — 2.\tEvaluate the Decision Tree model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.995\n",
      "F1 Score: 0.9939044100669591\n"
     ]
    }
   ],
   "source": [
    "X_test_scaled = scaler.transform(X_test)\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=X_test.columns)\n",
    "\n",
    "X_test_pca = pca.transform(X_test_scaled)\n",
    "\n",
    "y_pred = dt.predict(X_test_pca)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy:', accuracy)\n",
    "\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "print('F1 Score:', f1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Listing 9.49 Jupyter notebook code — Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "58222469-bd1e-4580-add0-be2029518c4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots()\n",
    "sns.heatmap(pd.DataFrame(confusion_matrix(y_test, y_pred)), annot=True, fmt='g')\n",
    "ax.set_ylabel('Actual Label')\n",
    "ax.set_xlabel('Predicted Label')\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
